{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Project\n",
    "- Data on sales from book retailers from 1992 to late 2019 was gathered from the Federal Reserve Economic Data (FRED).\n",
    "- The goal of this project was to use the book sales history to create a model that can forecast future sales into the end of 2020.\n",
    "- A model was created using an LSTM recurrent neural network, which was trained on a subset of the data and the results were compared to a test set.\n",
    "- Once satisfactory performance was achieved, a second LSTM model was trained using the entire data set, which was then used to project book sales of retailers into 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "Units:  Millions of Dollars, Not Seasonally Adjusted\n",
    "\n",
    "Frequency:  Monthly\n",
    "\n",
    "Information about the Monthly Retail Trade Survey can be found on the Census website at https://www.census.gov/retail/mrts/about_the_surveys.html\n",
    "\n",
    "Suggested Citation:\n",
    "U.S. Census Bureau, Retail Sales: Book Stores [MRTSSM451211USN], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MRTSSM451211USN, January 8, 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('MRTSSM451211USN.csv',parse_dates=True,index_col='DATE')\n",
    "df.columns = ['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            Sales\nDATE             \n1992-01-01    790\n1992-02-01    539\n1992-03-01    535\n1992-04-01    523\n1992-05-01    552\n1992-06-01    589\n1992-07-01    592\n1992-08-01    894\n1992-09-01    861\n1992-10-01    645\n1992-11-01    642\n1992-12-01   1165\n1993-01-01    998\n1993-02-01    568\n1993-03-01    602\n1993-04-01    583\n1993-05-01    612\n1993-06-01    618\n1993-07-01    607\n1993-08-01    983\n1993-09-01    903\n1993-10-01    669\n1993-11-01    692\n1993-12-01   1273\n1994-01-01   1053\n1994-02-01    635\n1994-03-01    634\n1994-04-01    610\n1994-05-01    684\n1994-06-01    724\n...           ...\n2017-05-01    761\n2017-06-01    683\n2017-07-01    647\n2017-08-01   1372\n2017-09-01    986\n2017-10-01    669\n2017-11-01    683\n2017-12-01   1151\n2018-01-01   1269\n2018-02-01    689\n2018-03-01    715\n2018-04-01    689\n2018-05-01    768\n2018-06-01    707\n2018-07-01    680\n2018-08-01   1368\n2018-09-01    976\n2018-10-01    722\n2018-11-01    752\n2018-12-01   1270\n2019-01-01   1136\n2019-02-01    644\n2019-03-01    664\n2019-04-01    713\n2019-05-01    763\n2019-06-01    672\n2019-07-01    646\n2019-08-01   1226\n2019-09-01    952\n2019-10-01    710\n\n[334 rows x 1 columns]",
      "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sales</th>\n    </tr>\n    <tr>\n      <th>DATE</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1992-01-01</th>\n      <td>790</td>\n    </tr>\n    <tr>\n      <th>1992-02-01</th>\n      <td>539</td>\n    </tr>\n    <tr>\n      <th>1992-03-01</th>\n      <td>535</td>\n    </tr>\n    <tr>\n      <th>1992-04-01</th>\n      <td>523</td>\n    </tr>\n    <tr>\n      <th>1992-05-01</th>\n      <td>552</td>\n    </tr>\n    <tr>\n      <th>1992-06-01</th>\n      <td>589</td>\n    </tr>\n    <tr>\n      <th>1992-07-01</th>\n      <td>592</td>\n    </tr>\n    <tr>\n      <th>1992-08-01</th>\n      <td>894</td>\n    </tr>\n    <tr>\n      <th>1992-09-01</th>\n      <td>861</td>\n    </tr>\n    <tr>\n      <th>1992-10-01</th>\n      <td>645</td>\n    </tr>\n    <tr>\n      <th>1992-11-01</th>\n      <td>642</td>\n    </tr>\n    <tr>\n      <th>1992-12-01</th>\n      <td>1165</td>\n    </tr>\n    <tr>\n      <th>1993-01-01</th>\n      <td>998</td>\n    </tr>\n    <tr>\n      <th>1993-02-01</th>\n      <td>568</td>\n    </tr>\n    <tr>\n      <th>1993-03-01</th>\n      <td>602</td>\n    </tr>\n    <tr>\n      <th>1993-04-01</th>\n      <td>583</td>\n    </tr>\n    <tr>\n      <th>1993-05-01</th>\n      <td>612</td>\n    </tr>\n    <tr>\n      <th>1993-06-01</th>\n      <td>618</td>\n    </tr>\n    <tr>\n      <th>1993-07-01</th>\n      <td>607</td>\n    </tr>\n    <tr>\n      <th>1993-08-01</th>\n      <td>983</td>\n    </tr>\n    <tr>\n      <th>1993-09-01</th>\n      <td>903</td>\n    </tr>\n    <tr>\n      <th>1993-10-01</th>\n      <td>669</td>\n    </tr>\n    <tr>\n      <th>1993-11-01</th>\n      <td>692</td>\n    </tr>\n    <tr>\n      <th>1993-12-01</th>\n      <td>1273</td>\n    </tr>\n    <tr>\n      <th>1994-01-01</th>\n      <td>1053</td>\n    </tr>\n    <tr>\n      <th>1994-02-01</th>\n      <td>635</td>\n    </tr>\n    <tr>\n      <th>1994-03-01</th>\n      <td>634</td>\n    </tr>\n    <tr>\n      <th>1994-04-01</th>\n      <td>610</td>\n    </tr>\n    <tr>\n      <th>1994-05-01</th>\n      <td>684</td>\n    </tr>\n    <tr>\n      <th>1994-06-01</th>\n      <td>724</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2017-05-01</th>\n      <td>761</td>\n    </tr>\n    <tr>\n      <th>2017-06-01</th>\n      <td>683</td>\n    </tr>\n    <tr>\n      <th>2017-07-01</th>\n      <td>647</td>\n    </tr>\n    <tr>\n      <th>2017-08-01</th>\n      <td>1372</td>\n    </tr>\n    <tr>\n      <th>2017-09-01</th>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>2017-10-01</th>\n      <td>669</td>\n    </tr>\n    <tr>\n      <th>2017-11-01</th>\n      <td>683</td>\n    </tr>\n    <tr>\n      <th>2017-12-01</th>\n      <td>1151</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>1269</td>\n    </tr>\n    <tr>\n      <th>2018-02-01</th>\n      <td>689</td>\n    </tr>\n    <tr>\n      <th>2018-03-01</th>\n      <td>715</td>\n    </tr>\n    <tr>\n      <th>2018-04-01</th>\n      <td>689</td>\n    </tr>\n    <tr>\n      <th>2018-05-01</th>\n      <td>768</td>\n    </tr>\n    <tr>\n      <th>2018-06-01</th>\n      <td>707</td>\n    </tr>\n    <tr>\n      <th>2018-07-01</th>\n      <td>680</td>\n    </tr>\n    <tr>\n      <th>2018-08-01</th>\n      <td>1368</td>\n    </tr>\n    <tr>\n      <th>2018-09-01</th>\n      <td>976</td>\n    </tr>\n    <tr>\n      <th>2018-10-01</th>\n      <td>722</td>\n    </tr>\n    <tr>\n      <th>2018-11-01</th>\n      <td>752</td>\n    </tr>\n    <tr>\n      <th>2018-12-01</th>\n      <td>1270</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>1136</td>\n    </tr>\n    <tr>\n      <th>2019-02-01</th>\n      <td>644</td>\n    </tr>\n    <tr>\n      <th>2019-03-01</th>\n      <td>664</td>\n    </tr>\n    <tr>\n      <th>2019-04-01</th>\n      <td>713</td>\n    </tr>\n    <tr>\n      <th>2019-05-01</th>\n      <td>763</td>\n    </tr>\n    <tr>\n      <th>2019-06-01</th>\n      <td>672</td>\n    </tr>\n    <tr>\n      <th>2019-07-01</th>\n      <td>646</td>\n    </tr>\n    <tr>\n      <th>2019-08-01</th>\n      <td>1226</td>\n    </tr>\n    <tr>\n      <th>2019-09-01</th>\n      <td>952</td>\n    </tr>\n    <tr>\n      <th>2019-10-01</th>\n      <td>710</td>\n    </tr>\n  </tbody>\n</table>\n<p>334 rows × 1 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 334 entries, 1992-01-01 to 2019-10-01\nData columns (total 1 columns):\nSales    334 non-null int64\ndtypes: int64(1)\nmemory usage: 5.2 KB\n"
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sales (in millions of USD) from 1992 to 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<matplotlib.figure.Figure at 0x1cf12246ac8>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (http://matplotlib.org/) -->\r\n<svg height=\"479pt\" version=\"1.1\" viewBox=\"0 0 936 479\" width=\"936pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n  <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n  </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 479.485156 \r\nL 936.44375 479.485156 \r\nL 936.44375 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n  </g>\r\n  <g id=\"axes_1\">\r\n   <g id=\"patch_2\">\r\n    <path d=\"M 36.44375 442.08 \r\nL 929.24375 442.08 \r\nL 929.24375 7.2 \r\nL 36.44375 7.2 \r\nz\r\n\" style=\"fill:#eaeaf2;\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_1\">\r\n    <g id=\"xtick_1\">\r\n     <g id=\"line2d_1\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 100.789696 442.08 \r\nL 100.789696 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_2\"/>\r\n     <g id=\"text_1\">\r\n      <!-- 1994 -->\r\n      <defs>\r\n       <path d=\"M 37.25 0 \r\nL 28.46875 0 \r\nL 28.46875 56 \r\nQ 25.296875 52.984375 20.140625 49.953125 \r\nQ 14.984375 46.921875 10.890625 45.40625 \r\nL 10.890625 53.90625 \r\nQ 18.265625 57.375 23.78125 62.296875 \r\nQ 29.296875 67.234375 31.59375 71.875 \r\nL 37.25 71.875 \r\nz\r\n\" id=\"ArialMT-31\"/>\r\n       <path d=\"M 5.46875 16.546875 \r\nL 13.921875 17.328125 \r\nQ 14.984375 11.375 18.015625 8.6875 \r\nQ 21.046875 6 25.78125 6 \r\nQ 29.828125 6 32.875 7.859375 \r\nQ 35.9375 9.71875 37.890625 12.8125 \r\nQ 39.84375 15.921875 41.15625 21.1875 \r\nQ 42.484375 26.46875 42.484375 31.9375 \r\nQ 42.484375 32.515625 42.4375 33.6875 \r\nQ 39.796875 29.5 35.234375 26.875 \r\nQ 30.671875 24.265625 25.34375 24.265625 \r\nQ 16.453125 24.265625 10.296875 30.703125 \r\nQ 4.15625 37.15625 4.15625 47.703125 \r\nQ 4.15625 58.59375 10.578125 65.234375 \r\nQ 17 71.875 26.65625 71.875 \r\nQ 33.640625 71.875 39.421875 68.109375 \r\nQ 45.21875 64.359375 48.21875 57.390625 \r\nQ 51.21875 50.4375 51.21875 37.25 \r\nQ 51.21875 23.53125 48.234375 15.40625 \r\nQ 45.265625 7.28125 39.375 3.03125 \r\nQ 33.5 -1.21875 25.59375 -1.21875 \r\nQ 17.1875 -1.21875 11.859375 3.4375 \r\nQ 6.546875 8.109375 5.46875 16.546875 \r\nz\r\nM 41.453125 48.140625 \r\nQ 41.453125 55.71875 37.421875 60.15625 \r\nQ 33.40625 64.59375 27.734375 64.59375 \r\nQ 21.875 64.59375 17.53125 59.8125 \r\nQ 13.1875 55.03125 13.1875 47.40625 \r\nQ 13.1875 40.578125 17.3125 36.296875 \r\nQ 21.4375 32.03125 27.484375 32.03125 \r\nQ 33.59375 32.03125 37.515625 36.296875 \r\nQ 41.453125 40.578125 41.453125 48.140625 \r\nz\r\n\" id=\"ArialMT-39\"/>\r\n       <path d=\"M 32.328125 0 \r\nL 32.328125 17.140625 \r\nL 1.265625 17.140625 \r\nL 1.265625 25.203125 \r\nL 33.9375 71.578125 \r\nL 41.109375 71.578125 \r\nL 41.109375 25.203125 \r\nL 50.78125 25.203125 \r\nL 50.78125 17.140625 \r\nL 41.109375 17.140625 \r\nL 41.109375 0 \r\nz\r\nM 32.328125 25.203125 \r\nL 32.328125 57.46875 \r\nL 9.90625 25.203125 \r\nz\r\n\" id=\"ArialMT-34\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(89.667821 456.237812)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-39\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-39\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_2\">\r\n     <g id=\"line2d_3\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 261.654561 442.08 \r\nL 261.654561 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_4\"/>\r\n     <g id=\"text_2\">\r\n      <!-- 1999 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(250.532686 456.237812)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-39\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-39\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-39\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_3\">\r\n     <g id=\"line2d_5\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 422.519426 442.08 \r\nL 422.519426 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_6\"/>\r\n     <g id=\"text_3\">\r\n      <!-- 2004 -->\r\n      <defs>\r\n       <path d=\"M 50.34375 8.453125 \r\nL 50.34375 0 \r\nL 3.03125 0 \r\nQ 2.9375 3.171875 4.046875 6.109375 \r\nQ 5.859375 10.9375 9.828125 15.625 \r\nQ 13.8125 20.3125 21.34375 26.46875 \r\nQ 33.015625 36.03125 37.109375 41.625 \r\nQ 41.21875 47.21875 41.21875 52.203125 \r\nQ 41.21875 57.421875 37.46875 61 \r\nQ 33.734375 64.59375 27.734375 64.59375 \r\nQ 21.390625 64.59375 17.578125 60.78125 \r\nQ 13.765625 56.984375 13.71875 50.25 \r\nL 4.6875 51.171875 \r\nQ 5.609375 61.28125 11.65625 66.578125 \r\nQ 17.71875 71.875 27.9375 71.875 \r\nQ 38.234375 71.875 44.234375 66.15625 \r\nQ 50.25 60.453125 50.25 52 \r\nQ 50.25 47.703125 48.484375 43.546875 \r\nQ 46.734375 39.40625 42.65625 34.8125 \r\nQ 38.578125 30.21875 29.109375 22.21875 \r\nQ 21.1875 15.578125 18.9375 13.203125 \r\nQ 16.703125 10.84375 15.234375 8.453125 \r\nz\r\n\" id=\"ArialMT-32\"/>\r\n       <path d=\"M 4.15625 35.296875 \r\nQ 4.15625 48 6.765625 55.734375 \r\nQ 9.375 63.484375 14.515625 67.671875 \r\nQ 19.671875 71.875 27.484375 71.875 \r\nQ 33.25 71.875 37.59375 69.546875 \r\nQ 41.9375 67.234375 44.765625 62.859375 \r\nQ 47.609375 58.5 49.21875 52.21875 \r\nQ 50.828125 45.953125 50.828125 35.296875 \r\nQ 50.828125 22.703125 48.234375 14.96875 \r\nQ 45.65625 7.234375 40.5 3 \r\nQ 35.359375 -1.21875 27.484375 -1.21875 \r\nQ 17.140625 -1.21875 11.234375 6.203125 \r\nQ 4.15625 15.140625 4.15625 35.296875 \r\nz\r\nM 13.1875 35.296875 \r\nQ 13.1875 17.671875 17.3125 11.828125 \r\nQ 21.4375 6 27.484375 6 \r\nQ 33.546875 6 37.671875 11.859375 \r\nQ 41.796875 17.71875 41.796875 35.296875 \r\nQ 41.796875 52.984375 37.671875 58.78125 \r\nQ 33.546875 64.59375 27.390625 64.59375 \r\nQ 21.34375 64.59375 17.71875 59.46875 \r\nQ 13.1875 52.9375 13.1875 35.296875 \r\nz\r\n\" id=\"ArialMT-30\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(411.397551 456.237812)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_4\">\r\n     <g id=\"line2d_7\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 583.384291 442.08 \r\nL 583.384291 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_8\"/>\r\n     <g id=\"text_4\">\r\n      <!-- 2009 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(572.262416 456.237812)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-39\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_5\">\r\n     <g id=\"line2d_9\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 744.249155 442.08 \r\nL 744.249155 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_10\"/>\r\n     <g id=\"text_5\">\r\n      <!-- 2014 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(733.12728 456.237812)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_6\">\r\n     <g id=\"line2d_11\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 905.11402 442.08 \r\nL 905.11402 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_12\"/>\r\n     <g id=\"text_6\">\r\n      <!-- 2019 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(893.992145 456.237812)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-39\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_7\">\r\n     <g id=\"line2d_13\"/>\r\n    </g>\r\n    <g id=\"xtick_8\">\r\n     <g id=\"line2d_14\"/>\r\n    </g>\r\n    <g id=\"xtick_9\">\r\n     <g id=\"line2d_15\"/>\r\n    </g>\r\n    <g id=\"xtick_10\">\r\n     <g id=\"line2d_16\"/>\r\n    </g>\r\n    <g id=\"xtick_11\">\r\n     <g id=\"line2d_17\"/>\r\n    </g>\r\n    <g id=\"xtick_12\">\r\n     <g id=\"line2d_18\"/>\r\n    </g>\r\n    <g id=\"xtick_13\">\r\n     <g id=\"line2d_19\"/>\r\n    </g>\r\n    <g id=\"xtick_14\">\r\n     <g id=\"line2d_20\"/>\r\n    </g>\r\n    <g id=\"xtick_15\">\r\n     <g id=\"line2d_21\"/>\r\n    </g>\r\n    <g id=\"xtick_16\">\r\n     <g id=\"line2d_22\"/>\r\n    </g>\r\n    <g id=\"xtick_17\">\r\n     <g id=\"line2d_23\"/>\r\n    </g>\r\n    <g id=\"xtick_18\">\r\n     <g id=\"line2d_24\"/>\r\n    </g>\r\n    <g id=\"xtick_19\">\r\n     <g id=\"line2d_25\"/>\r\n    </g>\r\n    <g id=\"xtick_20\">\r\n     <g id=\"line2d_26\"/>\r\n    </g>\r\n    <g id=\"xtick_21\">\r\n     <g id=\"line2d_27\"/>\r\n    </g>\r\n    <g id=\"xtick_22\">\r\n     <g id=\"line2d_28\"/>\r\n    </g>\r\n    <g id=\"xtick_23\">\r\n     <g id=\"line2d_29\"/>\r\n    </g>\r\n    <g id=\"xtick_24\">\r\n     <g id=\"line2d_30\"/>\r\n    </g>\r\n    <g id=\"xtick_25\">\r\n     <g id=\"line2d_31\"/>\r\n    </g>\r\n    <g id=\"xtick_26\">\r\n     <g id=\"line2d_32\"/>\r\n    </g>\r\n    <g id=\"xtick_27\">\r\n     <g id=\"line2d_33\"/>\r\n    </g>\r\n    <g id=\"xtick_28\">\r\n     <g id=\"line2d_34\"/>\r\n    </g>\r\n    <g id=\"xtick_29\">\r\n     <g id=\"line2d_35\"/>\r\n    </g>\r\n    <g id=\"xtick_30\">\r\n     <g id=\"line2d_36\"/>\r\n    </g>\r\n    <g id=\"xtick_31\">\r\n     <g id=\"line2d_37\"/>\r\n    </g>\r\n    <g id=\"xtick_32\">\r\n     <g id=\"line2d_38\"/>\r\n    </g>\r\n    <g id=\"xtick_33\">\r\n     <g id=\"line2d_39\"/>\r\n    </g>\r\n    <g id=\"xtick_34\">\r\n     <g id=\"line2d_40\"/>\r\n    </g>\r\n    <g id=\"text_7\">\r\n     <!-- DATE -->\r\n     <defs>\r\n      <path d=\"M 7.71875 0 \r\nL 7.71875 71.578125 \r\nL 32.375 71.578125 \r\nQ 40.71875 71.578125 45.125 70.5625 \r\nQ 51.265625 69.140625 55.609375 65.4375 \r\nQ 61.28125 60.640625 64.078125 53.1875 \r\nQ 66.890625 45.75 66.890625 36.1875 \r\nQ 66.890625 28.03125 64.984375 21.734375 \r\nQ 63.09375 15.4375 60.109375 11.296875 \r\nQ 57.125 7.171875 53.578125 4.796875 \r\nQ 50.046875 2.4375 45.046875 1.21875 \r\nQ 40.046875 0 33.546875 0 \r\nz\r\nM 17.1875 8.453125 \r\nL 32.46875 8.453125 \r\nQ 39.546875 8.453125 43.578125 9.765625 \r\nQ 47.609375 11.078125 50 13.484375 \r\nQ 53.375 16.84375 55.25 22.53125 \r\nQ 57.125 28.21875 57.125 36.328125 \r\nQ 57.125 47.5625 53.4375 53.59375 \r\nQ 49.75 59.625 44.484375 61.671875 \r\nQ 40.671875 63.140625 32.234375 63.140625 \r\nL 17.1875 63.140625 \r\nz\r\n\" id=\"ArialMT-44\"/>\r\n      <path d=\"M -0.140625 0 \r\nL 27.34375 71.578125 \r\nL 37.546875 71.578125 \r\nL 66.84375 0 \r\nL 56.0625 0 \r\nL 47.703125 21.6875 \r\nL 17.78125 21.6875 \r\nL 9.90625 0 \r\nz\r\nM 20.515625 29.390625 \r\nL 44.78125 29.390625 \r\nL 37.3125 49.21875 \r\nQ 33.890625 58.25 32.234375 64.0625 \r\nQ 30.859375 57.171875 28.375 50.390625 \r\nz\r\n\" id=\"ArialMT-41\"/>\r\n      <path d=\"M 25.921875 0 \r\nL 25.921875 63.140625 \r\nL 2.34375 63.140625 \r\nL 2.34375 71.578125 \r\nL 59.078125 71.578125 \r\nL 59.078125 63.140625 \r\nL 35.40625 63.140625 \r\nL 35.40625 0 \r\nz\r\n\" id=\"ArialMT-54\"/>\r\n      <path d=\"M 7.90625 0 \r\nL 7.90625 71.578125 \r\nL 59.671875 71.578125 \r\nL 59.671875 63.140625 \r\nL 17.390625 63.140625 \r\nL 17.390625 41.21875 \r\nL 56.984375 41.21875 \r\nL 56.984375 32.8125 \r\nL 17.390625 32.8125 \r\nL 17.390625 8.453125 \r\nL 61.328125 8.453125 \r\nL 61.328125 0 \r\nz\r\n\" id=\"ArialMT-45\"/>\r\n     </defs>\r\n     <g style=\"fill:#262626;\" transform=\"translate(468.181094 470.098906)scale(0.11 -0.11)\">\r\n      <use xlink:href=\"#ArialMT-44\"/>\r\n      <use x=\"72.216797\" xlink:href=\"#ArialMT-41\"/>\r\n      <use x=\"138.806641\" xlink:href=\"#ArialMT-54\"/>\r\n      <use x=\"199.890625\" xlink:href=\"#ArialMT-45\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_2\">\r\n    <g id=\"ytick_1\">\r\n     <g id=\"line2d_41\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 427.093456 \r\nL 929.24375 427.093456 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_42\"/>\r\n     <g id=\"text_8\">\r\n      <!-- 500 -->\r\n      <defs>\r\n       <path d=\"M 4.15625 18.75 \r\nL 13.375 19.53125 \r\nQ 14.40625 12.796875 18.140625 9.390625 \r\nQ 21.875 6 27.15625 6 \r\nQ 33.5 6 37.890625 10.78125 \r\nQ 42.28125 15.578125 42.28125 23.484375 \r\nQ 42.28125 31 38.0625 35.34375 \r\nQ 33.84375 39.703125 27 39.703125 \r\nQ 22.75 39.703125 19.328125 37.765625 \r\nQ 15.921875 35.84375 13.96875 32.765625 \r\nL 5.71875 33.84375 \r\nL 12.640625 70.609375 \r\nL 48.25 70.609375 \r\nL 48.25 62.203125 \r\nL 19.671875 62.203125 \r\nL 15.828125 42.96875 \r\nQ 22.265625 47.46875 29.34375 47.46875 \r\nQ 38.71875 47.46875 45.15625 40.96875 \r\nQ 51.609375 34.46875 51.609375 24.265625 \r\nQ 51.609375 14.546875 45.953125 7.46875 \r\nQ 39.0625 -1.21875 27.15625 -1.21875 \r\nQ 17.390625 -1.21875 11.203125 4.25 \r\nQ 5.03125 9.71875 4.15625 18.75 \r\nz\r\n\" id=\"ArialMT-35\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(12.760938 430.672362)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-35\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_2\">\r\n     <g id=\"line2d_43\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 375.129016 \r\nL 929.24375 375.129016 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_44\"/>\r\n     <g id=\"text_9\">\r\n      <!-- 750 -->\r\n      <defs>\r\n       <path d=\"M 4.734375 62.203125 \r\nL 4.734375 70.65625 \r\nL 51.078125 70.65625 \r\nL 51.078125 63.8125 \r\nQ 44.234375 56.546875 37.515625 44.484375 \r\nQ 30.8125 32.421875 27.15625 19.671875 \r\nQ 24.515625 10.6875 23.78125 0 \r\nL 14.75 0 \r\nQ 14.890625 8.453125 18.0625 20.40625 \r\nQ 21.234375 32.375 27.171875 43.484375 \r\nQ 33.109375 54.59375 39.796875 62.203125 \r\nz\r\n\" id=\"ArialMT-37\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(12.760938 378.707923)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-37\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-35\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_3\">\r\n     <g id=\"line2d_45\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 323.164577 \r\nL 929.24375 323.164577 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_46\"/>\r\n     <g id=\"text_10\">\r\n      <!-- 1000 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 326.743483)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_4\">\r\n     <g id=\"line2d_47\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 271.200138 \r\nL 929.24375 271.200138 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_48\"/>\r\n     <g id=\"text_11\">\r\n      <!-- 1250 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 274.779044)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-35\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_49\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 219.235698 \r\nL 929.24375 219.235698 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_50\"/>\r\n     <g id=\"text_12\">\r\n      <!-- 1500 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 222.814605)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-35\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_51\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 167.271259 \r\nL 929.24375 167.271259 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_52\"/>\r\n     <g id=\"text_13\">\r\n      <!-- 1750 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 170.850165)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-37\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-35\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_7\">\r\n     <g id=\"line2d_53\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 115.30682 \r\nL 929.24375 115.30682 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_54\"/>\r\n     <g id=\"text_14\">\r\n      <!-- 2000 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 118.885726)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_8\">\r\n     <g id=\"line2d_55\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 63.34238 \r\nL 929.24375 63.34238 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_56\"/>\r\n     <g id=\"text_15\">\r\n      <!-- 2250 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 66.921287)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-35\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_9\">\r\n     <g id=\"line2d_57\">\r\n      <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 11.377941 \r\nL 929.24375 11.377941 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_58\"/>\r\n     <g id=\"text_16\">\r\n      <!-- 2500 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 14.956847)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-35\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"line2d_59\">\r\n    <path clip-path=\"url(#p88fd4451e8)\" d=\"M 36.44375 366.814706 \r\nL 39.124831 418.987003 \r\nL 41.805912 419.818434 \r\nL 44.486993 422.312727 \r\nL 47.168074 416.284852 \r\nL 49.849155 408.594115 \r\nL 52.530236 407.970542 \r\nL 55.211318 345.197499 \r\nL 57.892399 352.056805 \r\nL 60.57348 396.954081 \r\nL 63.254561 397.577654 \r\nL 65.935642 288.868047 \r\nL 68.616723 323.580293 \r\nL 71.297804 412.959128 \r\nL 73.978885 405.891964 \r\nL 76.659966 409.841262 \r\nL 79.341047 403.813387 \r\nL 82.022128 402.56624 \r\nL 84.703209 404.852676 \r\nL 87.384291 326.698159 \r\nL 90.065372 343.326779 \r\nL 92.746453 391.965495 \r\nL 95.427534 387.184766 \r\nL 98.108615 266.419409 \r\nL 100.789696 312.148116 \r\nL 103.470777 399.032658 \r\nL 106.151858 399.240516 \r\nL 108.832939 404.229102 \r\nL 111.51402 388.847628 \r\nL 114.195101 380.533318 \r\nL 116.876182 390.094775 \r\nL 119.557264 291.154482 \r\nL 122.238345 318.591706 \r\nL 124.919426 378.870456 \r\nL 127.600507 370.556146 \r\nL 130.281588 238.150754 \r\nL 132.962669 259.144388 \r\nL 135.64375 381.780465 \r\nL 138.324831 386.561193 \r\nL 141.005912 388.016197 \r\nL 143.686993 367.853995 \r\nL 146.368074 363.073266 \r\nL 149.049155 368.477568 \r\nL 151.730236 271.615853 \r\nL 154.411318 301.963086 \r\nL 157.092399 375.960447 \r\nL 159.77348 354.343241 \r\nL 162.454561 226.302862 \r\nL 165.135642 245.633633 \r\nL 167.816723 373.674012 \r\nL 170.497804 376.584021 \r\nL 173.178885 375.129016 \r\nL 175.859966 353.719667 \r\nL 178.541047 356.837534 \r\nL 181.222128 367.646137 \r\nL 183.903209 248.543642 \r\nL 186.584291 314.850267 \r\nL 189.265372 344.15821 \r\nL 191.946453 342.287491 \r\nL 194.627534 198.65778 \r\nL 197.308615 207.179948 \r\nL 199.989696 359.539685 \r\nL 202.670777 362.033978 \r\nL 205.351858 363.488982 \r\nL 208.032939 345.197499 \r\nL 210.71402 349.146797 \r\nL 213.395101 358.500396 \r\nL 216.076182 260.599392 \r\nL 218.757264 290.323051 \r\nL 221.438345 339.169624 \r\nL 224.119426 324.411724 \r\nL 226.800507 164.153393 \r\nL 229.481588 226.926435 \r\nL 232.162669 339.58534 \r\nL 234.84375 347.691792 \r\nL 237.524831 353.51181 \r\nL 240.205912 340.001055 \r\nL 242.886993 335.8439 \r\nL 245.568074 347.89965 \r\nL 248.249155 243.13934 \r\nL 250.930236 272.031569 \r\nL 253.611318 332.933892 \r\nL 256.292399 325.243155 \r\nL 258.97348 146.693341 \r\nL 261.654561 216.32569 \r\nL 264.335642 325.243155 \r\nL 267.016723 328.153163 \r\nL 269.697804 337.091047 \r\nL 272.378885 328.153163 \r\nL 275.059966 316.720987 \r\nL 277.741047 318.383849 \r\nL 280.422128 242.931483 \r\nL 283.103209 273.278715 \r\nL 285.784291 319.21528 \r\nL 288.465372 304.873094 \r\nL 291.146453 116.138251 \r\nL 293.827534 218.19641 \r\nL 296.508615 304.457379 \r\nL 299.189696 316.720987 \r\nL 301.870777 328.361021 \r\nL 304.551858 301.54737 \r\nL 307.232939 297.805931 \r\nL 309.91402 313.187405 \r\nL 312.595101 199.697069 \r\nL 315.276182 238.774328 \r\nL 317.957264 320.254568 \r\nL 320.638345 302.794517 \r\nL 323.319426 137.755457 \r\nL 326.000507 201.567789 \r\nL 328.681588 308.614534 \r\nL 331.362669 310.277396 \r\nL 334.04375 336.883189 \r\nL 336.724831 308.614534 \r\nL 339.405912 307.15953 \r\nL 342.086993 322.125288 \r\nL 344.768074 152.721216 \r\nL 347.449155 238.774328 \r\nL 350.130236 320.046711 \r\nL 352.811318 302.794517 \r\nL 355.492399 124.452561 \r\nL 358.17348 121.334695 \r\nL 360.854561 323.164577 \r\nL 363.535642 321.501715 \r\nL 366.216723 330.231741 \r\nL 368.897804 299.053077 \r\nL 371.578885 313.810978 \r\nL 374.259966 315.265982 \r\nL 376.941047 157.501944 \r\nL 379.622128 221.314276 \r\nL 382.303209 323.78815 \r\nL 384.984291 310.900969 \r\nL 387.665372 125.699707 \r\nL 390.346453 103.666785 \r\nL 393.027534 325.035297 \r\nL 395.708615 338.753909 \r\nL 398.389696 328.361021 \r\nL 401.070777 300.923797 \r\nL 403.751858 285.542323 \r\nL 406.432939 296.558784 \r\nL 409.11402 86.414591 \r\nL 411.795101 204.26994 \r\nL 414.476182 313.810978 \r\nL 417.157264 315.681698 \r\nL 419.838345 102.003923 \r\nL 422.519426 84.959587 \r\nL 425.200507 300.715939 \r\nL 427.881588 307.990961 \r\nL 430.562669 318.383849 \r\nL 433.24375 299.67665 \r\nL 435.924831 280.138021 \r\nL 438.605912 287.413043 \r\nL 441.286993 90.363889 \r\nL 443.968074 206.556375 \r\nL 446.649155 308.822392 \r\nL 449.330236 304.665237 \r\nL 452.011318 73.735268 \r\nL 454.692399 99.50963 \r\nL 457.37348 305.28881 \r\nL 460.054561 302.170944 \r\nL 462.735642 323.78815 \r\nL 465.416723 298.221646 \r\nL 468.097804 291.154482 \r\nL 470.778885 278.683017 \r\nL 473.459966 69.993829 \r\nL 476.141047 208.011379 \r\nL 478.822128 309.861681 \r\nL 481.503209 291.778056 \r\nL 484.184291 61.887376 \r\nL 486.865372 68.538824 \r\nL 489.546453 302.170944 \r\nL 492.227534 304.041663 \r\nL 494.908615 325.866728 \r\nL 497.589696 286.581612 \r\nL 500.270777 273.694431 \r\nL 502.951858 295.10378 \r\nL 505.632939 85.791018 \r\nL 508.31402 208.011379 \r\nL 510.995101 311.940258 \r\nL 513.676182 293.648775 \r\nL 516.357264 87.869596 \r\nL 519.038345 69.578113 \r\nL 521.719426 315.058124 \r\nL 524.400507 317.552418 \r\nL 527.081588 337.71462 \r\nL 529.762669 295.311638 \r\nL 532.44375 289.699478 \r\nL 535.124831 277.43587 \r\nL 537.805912 47.337333 \r\nL 540.486993 203.022793 \r\nL 543.168074 296.350926 \r\nL 545.849155 275.980866 \r\nL 548.530236 78.100281 \r\nL 551.211318 52.949492 \r\nL 553.892399 290.530909 \r\nL 556.57348 317.760275 \r\nL 559.254561 322.333146 \r\nL 561.935642 290.323051 \r\nL 564.616723 307.990961 \r\nL 567.297804 298.013788 \r\nL 569.978885 26.967273 \r\nL 572.659966 225.263573 \r\nL 575.341047 318.175991 \r\nL 578.022128 316.097413 \r\nL 580.703209 113.643958 \r\nL 583.384291 76.021703 \r\nL 586.065372 329.40031 \r\nL 588.746453 334.596754 \r\nL 591.427534 341.248202 \r\nL 594.108615 312.148116 \r\nL 596.789696 314.850267 \r\nL 599.470777 315.265982 \r\nL 602.151858 53.780923 \r\nL 604.832939 214.662828 \r\nL 607.51402 331.478887 \r\nL 610.195101 330.439599 \r\nL 612.876182 133.80616 \r\nL 615.557264 84.128156 \r\nL 618.238345 333.97318 \r\nL 620.919426 333.97318 \r\nL 623.600507 352.680379 \r\nL 626.281588 320.046711 \r\nL 628.962669 319.007422 \r\nL 631.64375 322.541004 \r\nL 634.324831 87.45388 \r\nL 637.005912 240.021474 \r\nL 639.686993 342.911064 \r\nL 642.368074 327.737448 \r\nL 645.049155 140.457608 \r\nL 647.730236 125.699707 \r\nL 650.411318 331.478887 \r\nL 653.092399 347.89965 \r\nL 655.77348 365.359702 \r\nL 658.454561 336.467473 \r\nL 661.135642 343.534637 \r\nL 663.816723 351.225374 \r\nL 666.497804 85.791018 \r\nL 669.178885 253.532228 \r\nL 671.859966 374.921159 \r\nL 674.541047 365.359702 \r\nL 677.222128 240.021474 \r\nL 679.903209 174.338423 \r\nL 682.584291 360.371116 \r\nL 685.265372 373.88187 \r\nL 687.946453 376.791878 \r\nL 690.627534 346.652504 \r\nL 693.308615 356.629676 \r\nL 695.989696 367.853995 \r\nL 698.670777 149.395492 \r\nL 701.351858 303.002375 \r\nL 704.032939 381.988322 \r\nL 706.71402 374.089728 \r\nL 709.395101 257.06581 \r\nL 712.076182 186.394173 \r\nL 714.757264 376.999736 \r\nL 717.438345 384.0669 \r\nL 720.119426 393.004783 \r\nL 722.800507 360.371116 \r\nL 725.481588 380.533318 \r\nL 728.162669 384.274758 \r\nL 730.84375 204.893513 \r\nL 733.524831 300.715939 \r\nL 736.205912 381.364749 \r\nL 738.886993 376.376163 \r\nL 741.568074 255.19509 \r\nL 744.249155 222.977138 \r\nL 746.930236 379.494029 \r\nL 749.611318 389.055486 \r\nL 752.292399 389.471202 \r\nL 754.97348 366.398991 \r\nL 757.654561 385.521904 \r\nL 760.335642 384.690473 \r\nL 763.016723 177.664147 \r\nL 765.697804 309.030249 \r\nL 768.378885 380.117603 \r\nL 771.059966 379.701887 \r\nL 773.741047 254.155802 \r\nL 776.422128 222.353565 \r\nL 779.103209 388.224055 \r\nL 781.784291 394.459788 \r\nL 784.465372 395.083361 \r\nL 787.146453 374.713301 \r\nL 789.827534 388.639771 \r\nL 792.508615 386.561193 \r\nL 795.189696 208.84281 \r\nL 797.870777 309.445965 \r\nL 800.551858 381.364749 \r\nL 803.232939 380.117603 \r\nL 805.91402 256.442237 \r\nL 808.595101 233.993599 \r\nL 811.276182 387.392624 \r\nL 813.957264 388.847628 \r\nL 816.638345 390.718348 \r\nL 819.319426 376.168305 \r\nL 822.000507 389.679059 \r\nL 824.681588 394.875503 \r\nL 827.362669 218.819983 \r\nL 830.04375 315.058124 \r\nL 832.724831 388.431913 \r\nL 835.405912 382.404038 \r\nL 838.086993 273.486573 \r\nL 840.768074 245.633633 \r\nL 843.449155 392.589068 \r\nL 846.130236 388.847628 \r\nL 848.811318 384.0669 \r\nL 851.492399 372.842581 \r\nL 854.17348 389.055486 \r\nL 856.854561 396.538365 \r\nL 859.535642 245.841491 \r\nL 862.216723 326.074586 \r\nL 864.897804 391.965495 \r\nL 867.578885 389.055486 \r\nL 870.259966 291.778056 \r\nL 872.941047 267.25084 \r\nL 875.622128 387.80834 \r\nL 878.303209 382.404038 \r\nL 880.984291 387.80834 \r\nL 883.665372 371.387577 \r\nL 886.346453 384.0669 \r\nL 889.027534 389.679059 \r\nL 891.708615 246.672922 \r\nL 894.389696 328.153163 \r\nL 897.070777 380.949034 \r\nL 899.751858 374.713301 \r\nL 902.432939 267.042983 \r\nL 905.11402 294.895922 \r\nL 907.795101 397.161939 \r\nL 910.476182 393.004783 \r\nL 915.838345 372.426866 \r\nL 918.519426 391.341921 \r\nL 921.200507 396.746223 \r\nL 923.881588 276.188724 \r\nL 926.562669 333.141749 \r\nL 929.24375 383.443327 \r\nL 929.24375 383.443327 \r\n\" style=\"fill:none;stroke:#4c72b0;stroke-linecap:round;stroke-width:1.75;\"/>\r\n   </g>\r\n   <g id=\"patch_3\">\r\n    <path d=\"M 36.44375 442.08 \r\nL 36.44375 7.2 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_4\">\r\n    <path d=\"M 929.24375 442.08 \r\nL 929.24375 7.2 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_5\">\r\n    <path d=\"M 36.44375 442.08 \r\nL 929.24375 442.08 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_6\">\r\n    <path d=\"M 36.44375 7.2 \r\nL 929.24375 7.2 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"legend_1\">\r\n    <g id=\"line2d_60\">\r\n     <path d=\"M 867.229687 19.857812 \r\nL 887.229687 19.857812 \r\n\" style=\"fill:none;stroke:#4c72b0;stroke-linecap:round;stroke-width:1.75;\"/>\r\n    </g>\r\n    <g id=\"line2d_61\"/>\r\n    <g id=\"text_17\">\r\n     <!-- Sales -->\r\n     <defs>\r\n      <path d=\"M 4.5 23 \r\nL 13.421875 23.78125 \r\nQ 14.0625 18.40625 16.375 14.96875 \r\nQ 18.703125 11.53125 23.578125 9.40625 \r\nQ 28.46875 7.28125 34.578125 7.28125 \r\nQ 39.984375 7.28125 44.140625 8.890625 \r\nQ 48.296875 10.5 50.3125 13.296875 \r\nQ 52.34375 16.109375 52.34375 19.4375 \r\nQ 52.34375 22.796875 50.390625 25.3125 \r\nQ 48.4375 27.828125 43.953125 29.546875 \r\nQ 41.0625 30.671875 31.203125 33.03125 \r\nQ 21.34375 35.40625 17.390625 37.5 \r\nQ 12.25 40.1875 9.734375 44.15625 \r\nQ 7.234375 48.140625 7.234375 53.078125 \r\nQ 7.234375 58.5 10.296875 63.203125 \r\nQ 13.375 67.921875 19.28125 70.359375 \r\nQ 25.203125 72.796875 32.421875 72.796875 \r\nQ 40.375 72.796875 46.453125 70.234375 \r\nQ 52.546875 67.671875 55.8125 62.6875 \r\nQ 59.078125 57.71875 59.328125 51.421875 \r\nL 50.25 50.734375 \r\nQ 49.515625 57.515625 45.28125 60.984375 \r\nQ 41.0625 64.453125 32.8125 64.453125 \r\nQ 24.21875 64.453125 20.28125 61.296875 \r\nQ 16.359375 58.15625 16.359375 53.71875 \r\nQ 16.359375 49.859375 19.140625 47.359375 \r\nQ 21.875 44.875 33.421875 42.265625 \r\nQ 44.96875 39.65625 49.265625 37.703125 \r\nQ 55.515625 34.8125 58.484375 30.390625 \r\nQ 61.46875 25.984375 61.46875 20.21875 \r\nQ 61.46875 14.5 58.203125 9.4375 \r\nQ 54.9375 4.390625 48.796875 1.578125 \r\nQ 42.671875 -1.21875 35.015625 -1.21875 \r\nQ 25.296875 -1.21875 18.71875 1.609375 \r\nQ 12.15625 4.4375 8.421875 10.125 \r\nQ 4.6875 15.828125 4.5 23 \r\nz\r\n\" id=\"ArialMT-53\"/>\r\n      <path d=\"M 40.4375 6.390625 \r\nQ 35.546875 2.25 31.03125 0.53125 \r\nQ 26.515625 -1.171875 21.34375 -1.171875 \r\nQ 12.796875 -1.171875 8.203125 3 \r\nQ 3.609375 7.171875 3.609375 13.671875 \r\nQ 3.609375 17.484375 5.34375 20.625 \r\nQ 7.078125 23.78125 9.890625 25.6875 \r\nQ 12.703125 27.59375 16.21875 28.5625 \r\nQ 18.796875 29.25 24.03125 29.890625 \r\nQ 34.671875 31.15625 39.703125 32.90625 \r\nQ 39.75 34.71875 39.75 35.203125 \r\nQ 39.75 40.578125 37.25 42.78125 \r\nQ 33.890625 45.75 27.25 45.75 \r\nQ 21.046875 45.75 18.09375 43.578125 \r\nQ 15.140625 41.40625 13.71875 35.890625 \r\nL 5.125 37.0625 \r\nQ 6.296875 42.578125 8.984375 45.96875 \r\nQ 11.671875 49.359375 16.75 51.1875 \r\nQ 21.828125 53.03125 28.515625 53.03125 \r\nQ 35.15625 53.03125 39.296875 51.46875 \r\nQ 43.453125 49.90625 45.40625 47.53125 \r\nQ 47.359375 45.171875 48.140625 41.546875 \r\nQ 48.578125 39.3125 48.578125 33.453125 \r\nL 48.578125 21.734375 \r\nQ 48.578125 9.46875 49.140625 6.21875 \r\nQ 49.703125 2.984375 51.375 0 \r\nL 42.1875 0 \r\nQ 40.828125 2.734375 40.4375 6.390625 \r\nz\r\nM 39.703125 26.03125 \r\nQ 34.90625 24.078125 25.34375 22.703125 \r\nQ 19.921875 21.921875 17.671875 20.9375 \r\nQ 15.4375 19.96875 14.203125 18.09375 \r\nQ 12.984375 16.21875 12.984375 13.921875 \r\nQ 12.984375 10.40625 15.640625 8.0625 \r\nQ 18.3125 5.71875 23.4375 5.71875 \r\nQ 28.515625 5.71875 32.46875 7.9375 \r\nQ 36.421875 10.15625 38.28125 14.015625 \r\nQ 39.703125 17 39.703125 22.796875 \r\nz\r\n\" id=\"ArialMT-61\"/>\r\n      <path d=\"M 6.390625 0 \r\nL 6.390625 71.578125 \r\nL 15.1875 71.578125 \r\nL 15.1875 0 \r\nz\r\n\" id=\"ArialMT-6c\"/>\r\n      <path d=\"M 42.09375 16.703125 \r\nL 51.171875 15.578125 \r\nQ 49.03125 7.625 43.21875 3.21875 \r\nQ 37.40625 -1.171875 28.375 -1.171875 \r\nQ 17 -1.171875 10.328125 5.828125 \r\nQ 3.65625 12.84375 3.65625 25.484375 \r\nQ 3.65625 38.578125 10.390625 45.796875 \r\nQ 17.140625 53.03125 27.875 53.03125 \r\nQ 38.28125 53.03125 44.875 45.953125 \r\nQ 51.46875 38.875 51.46875 26.03125 \r\nQ 51.46875 25.25 51.421875 23.6875 \r\nL 12.75 23.6875 \r\nQ 13.234375 15.140625 17.578125 10.59375 \r\nQ 21.921875 6.0625 28.421875 6.0625 \r\nQ 33.25 6.0625 36.671875 8.59375 \r\nQ 40.09375 11.140625 42.09375 16.703125 \r\nz\r\nM 13.234375 30.90625 \r\nL 42.1875 30.90625 \r\nQ 41.609375 37.453125 38.875 40.71875 \r\nQ 34.671875 45.796875 27.984375 45.796875 \r\nQ 21.921875 45.796875 17.796875 41.75 \r\nQ 13.671875 37.703125 13.234375 30.90625 \r\nz\r\n\" id=\"ArialMT-65\"/>\r\n      <path d=\"M 3.078125 15.484375 \r\nL 11.765625 16.84375 \r\nQ 12.5 11.625 15.84375 8.84375 \r\nQ 19.1875 6.0625 25.203125 6.0625 \r\nQ 31.25 6.0625 34.171875 8.515625 \r\nQ 37.109375 10.984375 37.109375 14.3125 \r\nQ 37.109375 17.28125 34.515625 19 \r\nQ 32.71875 20.171875 25.53125 21.96875 \r\nQ 15.875 24.421875 12.140625 26.203125 \r\nQ 8.40625 27.984375 6.46875 31.125 \r\nQ 4.546875 34.28125 4.546875 38.09375 \r\nQ 4.546875 41.546875 6.125 44.5 \r\nQ 7.71875 47.46875 10.453125 49.421875 \r\nQ 12.5 50.921875 16.03125 51.96875 \r\nQ 19.578125 53.03125 23.640625 53.03125 \r\nQ 29.734375 53.03125 34.34375 51.265625 \r\nQ 38.96875 49.515625 41.15625 46.5 \r\nQ 43.359375 43.5 44.1875 38.484375 \r\nL 35.59375 37.3125 \r\nQ 35.015625 41.3125 32.203125 43.546875 \r\nQ 29.390625 45.796875 24.265625 45.796875 \r\nQ 18.21875 45.796875 15.625 43.796875 \r\nQ 13.03125 41.796875 13.03125 39.109375 \r\nQ 13.03125 37.40625 14.109375 36.03125 \r\nQ 15.1875 34.625 17.484375 33.6875 \r\nQ 18.796875 33.203125 25.25 31.453125 \r\nQ 34.578125 28.953125 38.25 27.359375 \r\nQ 41.9375 25.78125 44.03125 22.75 \r\nQ 46.140625 19.734375 46.140625 15.234375 \r\nQ 46.140625 10.84375 43.578125 6.953125 \r\nQ 41.015625 3.078125 36.171875 0.953125 \r\nQ 31.34375 -1.171875 25.25 -1.171875 \r\nQ 15.140625 -1.171875 9.84375 3.03125 \r\nQ 4.546875 7.234375 3.078125 15.484375 \r\nz\r\n\" id=\"ArialMT-73\"/>\r\n     </defs>\r\n     <g style=\"fill:#262626;\" transform=\"translate(895.229687 23.357812)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#ArialMT-53\"/>\r\n      <use x=\"66.699219\" xlink:href=\"#ArialMT-61\"/>\r\n      <use x=\"122.314453\" xlink:href=\"#ArialMT-6c\"/>\r\n      <use x=\"144.53125\" xlink:href=\"#ArialMT-65\"/>\r\n      <use x=\"200.146484\" xlink:href=\"#ArialMT-73\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"p88fd4451e8\">\r\n   <rect height=\"434.88\" width=\"892.8\" x=\"36.44375\" y=\"7.2\"/>\r\n  </clipPath>\r\n </defs>\r\n</svg>\r\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAHfCAYAAABd+fV4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhWMAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "df.plot(figsize=(16,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sales in the most recent 36 months**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<matplotlib.figure.Figure at 0x1cf122b4c88>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (http://matplotlib.org/) -->\r\n<svg height=\"493pt\" version=\"1.1\" viewBox=\"0 0 936 493\" width=\"936pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n  <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n  </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 493.640938 \r\nL 936.44375 493.640938 \r\nL 936.44375 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n  </g>\r\n  <g id=\"axes_1\">\r\n   <g id=\"patch_2\">\r\n    <path d=\"M 36.44375 445.658906 \r\nL 929.24375 445.658906 \r\nL 929.24375 10.778906 \r\nL 36.44375 10.778906 \r\nz\r\n\" style=\"fill:#eaeaf2;\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_1\">\r\n    <g id=\"xtick_1\">\r\n     <g id=\"line2d_1\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 110.84375 445.658906 \r\nL 110.84375 10.778906 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_2\"/>\r\n     <g id=\"text_1\">\r\n      <!-- Jan -->\r\n      <defs>\r\n       <path d=\"M 2.875 20.3125 \r\nL 11.421875 21.484375 \r\nQ 11.765625 13.28125 14.5 10.25 \r\nQ 17.234375 7.234375 22.078125 7.234375 \r\nQ 25.640625 7.234375 28.21875 8.859375 \r\nQ 30.8125 10.5 31.78125 13.296875 \r\nQ 32.765625 16.109375 32.765625 22.265625 \r\nL 32.765625 71.578125 \r\nL 42.234375 71.578125 \r\nL 42.234375 22.796875 \r\nQ 42.234375 13.8125 40.0625 8.875 \r\nQ 37.890625 3.953125 33.171875 1.359375 \r\nQ 28.46875 -1.21875 22.125 -1.21875 \r\nQ 12.703125 -1.21875 7.6875 4.203125 \r\nQ 2.6875 9.625 2.875 20.3125 \r\nz\r\n\" id=\"ArialMT-4a\"/>\r\n       <path d=\"M 40.4375 6.390625 \r\nQ 35.546875 2.25 31.03125 0.53125 \r\nQ 26.515625 -1.171875 21.34375 -1.171875 \r\nQ 12.796875 -1.171875 8.203125 3 \r\nQ 3.609375 7.171875 3.609375 13.671875 \r\nQ 3.609375 17.484375 5.34375 20.625 \r\nQ 7.078125 23.78125 9.890625 25.6875 \r\nQ 12.703125 27.59375 16.21875 28.5625 \r\nQ 18.796875 29.25 24.03125 29.890625 \r\nQ 34.671875 31.15625 39.703125 32.90625 \r\nQ 39.75 34.71875 39.75 35.203125 \r\nQ 39.75 40.578125 37.25 42.78125 \r\nQ 33.890625 45.75 27.25 45.75 \r\nQ 21.046875 45.75 18.09375 43.578125 \r\nQ 15.140625 41.40625 13.71875 35.890625 \r\nL 5.125 37.0625 \r\nQ 6.296875 42.578125 8.984375 45.96875 \r\nQ 11.671875 49.359375 16.75 51.1875 \r\nQ 21.828125 53.03125 28.515625 53.03125 \r\nQ 35.15625 53.03125 39.296875 51.46875 \r\nQ 43.453125 49.90625 45.40625 47.53125 \r\nQ 47.359375 45.171875 48.140625 41.546875 \r\nQ 48.578125 39.3125 48.578125 33.453125 \r\nL 48.578125 21.734375 \r\nQ 48.578125 9.46875 49.140625 6.21875 \r\nQ 49.703125 2.984375 51.375 0 \r\nL 42.1875 0 \r\nQ 40.828125 2.734375 40.4375 6.390625 \r\nz\r\nM 39.703125 26.03125 \r\nQ 34.90625 24.078125 25.34375 22.703125 \r\nQ 19.921875 21.921875 17.671875 20.9375 \r\nQ 15.4375 19.96875 14.203125 18.09375 \r\nQ 12.984375 16.21875 12.984375 13.921875 \r\nQ 12.984375 10.40625 15.640625 8.0625 \r\nQ 18.3125 5.71875 23.4375 5.71875 \r\nQ 28.515625 5.71875 32.46875 7.9375 \r\nQ 36.421875 10.15625 38.28125 14.015625 \r\nQ 39.703125 17 39.703125 22.796875 \r\nz\r\n\" id=\"ArialMT-61\"/>\r\n       <path d=\"M 6.59375 0 \r\nL 6.59375 51.859375 \r\nL 14.5 51.859375 \r\nL 14.5 44.484375 \r\nQ 20.21875 53.03125 31 53.03125 \r\nQ 35.6875 53.03125 39.625 51.34375 \r\nQ 43.5625 49.65625 45.515625 46.921875 \r\nQ 47.46875 44.1875 48.25 40.4375 \r\nQ 48.734375 37.984375 48.734375 31.890625 \r\nL 48.734375 0 \r\nL 39.9375 0 \r\nL 39.9375 31.546875 \r\nQ 39.9375 36.921875 38.90625 39.578125 \r\nQ 37.890625 42.234375 35.28125 43.8125 \r\nQ 32.671875 45.40625 29.15625 45.40625 \r\nQ 23.53125 45.40625 19.453125 41.84375 \r\nQ 15.375 38.28125 15.375 28.328125 \r\nL 15.375 0 \r\nz\r\n\" id=\"ArialMT-6e\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(102.782813 459.816719)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-4a\"/>\r\n       <use x=\"50\" xlink:href=\"#ArialMT-61\"/>\r\n       <use x=\"105.615234\" xlink:href=\"#ArialMT-6e\"/>\r\n      </g>\r\n      <!-- 2017 -->\r\n      <defs>\r\n       <path d=\"M 50.34375 8.453125 \r\nL 50.34375 0 \r\nL 3.03125 0 \r\nQ 2.9375 3.171875 4.046875 6.109375 \r\nQ 5.859375 10.9375 9.828125 15.625 \r\nQ 13.8125 20.3125 21.34375 26.46875 \r\nQ 33.015625 36.03125 37.109375 41.625 \r\nQ 41.21875 47.21875 41.21875 52.203125 \r\nQ 41.21875 57.421875 37.46875 61 \r\nQ 33.734375 64.59375 27.734375 64.59375 \r\nQ 21.390625 64.59375 17.578125 60.78125 \r\nQ 13.765625 56.984375 13.71875 50.25 \r\nL 4.6875 51.171875 \r\nQ 5.609375 61.28125 11.65625 66.578125 \r\nQ 17.71875 71.875 27.9375 71.875 \r\nQ 38.234375 71.875 44.234375 66.15625 \r\nQ 50.25 60.453125 50.25 52 \r\nQ 50.25 47.703125 48.484375 43.546875 \r\nQ 46.734375 39.40625 42.65625 34.8125 \r\nQ 38.578125 30.21875 29.109375 22.21875 \r\nQ 21.1875 15.578125 18.9375 13.203125 \r\nQ 16.703125 10.84375 15.234375 8.453125 \r\nz\r\n\" id=\"ArialMT-32\"/>\r\n       <path d=\"M 4.15625 35.296875 \r\nQ 4.15625 48 6.765625 55.734375 \r\nQ 9.375 63.484375 14.515625 67.671875 \r\nQ 19.671875 71.875 27.484375 71.875 \r\nQ 33.25 71.875 37.59375 69.546875 \r\nQ 41.9375 67.234375 44.765625 62.859375 \r\nQ 47.609375 58.5 49.21875 52.21875 \r\nQ 50.828125 45.953125 50.828125 35.296875 \r\nQ 50.828125 22.703125 48.234375 14.96875 \r\nQ 45.65625 7.234375 40.5 3 \r\nQ 35.359375 -1.21875 27.484375 -1.21875 \r\nQ 17.140625 -1.21875 11.234375 6.203125 \r\nQ 4.15625 15.140625 4.15625 35.296875 \r\nz\r\nM 13.1875 35.296875 \r\nQ 13.1875 17.671875 17.3125 11.828125 \r\nQ 21.4375 6 27.484375 6 \r\nQ 33.546875 6 37.671875 11.859375 \r\nQ 41.796875 17.71875 41.796875 35.296875 \r\nQ 41.796875 52.984375 37.671875 58.78125 \r\nQ 33.546875 64.59375 27.390625 64.59375 \r\nQ 21.34375 64.59375 17.71875 59.46875 \r\nQ 13.1875 52.9375 13.1875 35.296875 \r\nz\r\n\" id=\"ArialMT-30\"/>\r\n       <path d=\"M 37.25 0 \r\nL 28.46875 0 \r\nL 28.46875 56 \r\nQ 25.296875 52.984375 20.140625 49.953125 \r\nQ 14.984375 46.921875 10.890625 45.40625 \r\nL 10.890625 53.90625 \r\nQ 18.265625 57.375 23.78125 62.296875 \r\nQ 29.296875 67.234375 31.59375 71.875 \r\nL 37.25 71.875 \r\nz\r\n\" id=\"ArialMT-31\"/>\r\n       <path d=\"M 4.734375 62.203125 \r\nL 4.734375 70.65625 \r\nL 51.078125 70.65625 \r\nL 51.078125 63.8125 \r\nQ 44.234375 56.546875 37.515625 44.484375 \r\nQ 30.8125 32.421875 27.15625 19.671875 \r\nQ 24.515625 10.6875 23.78125 0 \r\nL 14.75 0 \r\nQ 14.890625 8.453125 18.0625 20.40625 \r\nQ 21.234375 32.375 27.171875 43.484375 \r\nQ 33.109375 54.59375 39.796875 62.203125 \r\nz\r\n\" id=\"ArialMT-37\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(99.721875 470.393594)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-37\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_2\">\r\n     <g id=\"line2d_3\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 408.44375 445.658906 \r\nL 408.44375 10.778906 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_4\"/>\r\n     <g id=\"text_2\">\r\n      <!-- Jan -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(400.382813 459.816719)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-4a\"/>\r\n       <use x=\"50\" xlink:href=\"#ArialMT-61\"/>\r\n       <use x=\"105.615234\" xlink:href=\"#ArialMT-6e\"/>\r\n      </g>\r\n      <!-- 2018 -->\r\n      <defs>\r\n       <path d=\"M 17.671875 38.8125 \r\nQ 12.203125 40.828125 9.5625 44.53125 \r\nQ 6.9375 48.25 6.9375 53.421875 \r\nQ 6.9375 61.234375 12.546875 66.546875 \r\nQ 18.171875 71.875 27.484375 71.875 \r\nQ 36.859375 71.875 42.578125 66.421875 \r\nQ 48.296875 60.984375 48.296875 53.171875 \r\nQ 48.296875 48.1875 45.671875 44.5 \r\nQ 43.0625 40.828125 37.75 38.8125 \r\nQ 44.34375 36.671875 47.78125 31.875 \r\nQ 51.21875 27.09375 51.21875 20.453125 \r\nQ 51.21875 11.28125 44.71875 5.03125 \r\nQ 38.234375 -1.21875 27.640625 -1.21875 \r\nQ 17.046875 -1.21875 10.546875 5.046875 \r\nQ 4.046875 11.328125 4.046875 20.703125 \r\nQ 4.046875 27.6875 7.59375 32.390625 \r\nQ 11.140625 37.109375 17.671875 38.8125 \r\nz\r\nM 15.921875 53.71875 \r\nQ 15.921875 48.640625 19.1875 45.40625 \r\nQ 22.46875 42.1875 27.6875 42.1875 \r\nQ 32.765625 42.1875 36.015625 45.375 \r\nQ 39.265625 48.578125 39.265625 53.21875 \r\nQ 39.265625 58.0625 35.90625 61.359375 \r\nQ 32.5625 64.65625 27.59375 64.65625 \r\nQ 22.5625 64.65625 19.234375 61.421875 \r\nQ 15.921875 58.203125 15.921875 53.71875 \r\nz\r\nM 13.09375 20.65625 \r\nQ 13.09375 16.890625 14.875 13.375 \r\nQ 16.65625 9.859375 20.171875 7.921875 \r\nQ 23.6875 6 27.734375 6 \r\nQ 34.03125 6 38.125 10.046875 \r\nQ 42.234375 14.109375 42.234375 20.359375 \r\nQ 42.234375 26.703125 38.015625 30.859375 \r\nQ 33.796875 35.015625 27.4375 35.015625 \r\nQ 21.234375 35.015625 17.15625 30.90625 \r\nQ 13.09375 26.8125 13.09375 20.65625 \r\nz\r\n\" id=\"ArialMT-38\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(397.321875 470.393594)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-38\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_3\">\r\n     <g id=\"line2d_5\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 706.04375 445.658906 \r\nL 706.04375 10.778906 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_6\"/>\r\n     <g id=\"text_3\">\r\n      <!-- Jan -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(697.982813 459.816719)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-4a\"/>\r\n       <use x=\"50\" xlink:href=\"#ArialMT-61\"/>\r\n       <use x=\"105.615234\" xlink:href=\"#ArialMT-6e\"/>\r\n      </g>\r\n      <!-- 2019 -->\r\n      <defs>\r\n       <path d=\"M 5.46875 16.546875 \r\nL 13.921875 17.328125 \r\nQ 14.984375 11.375 18.015625 8.6875 \r\nQ 21.046875 6 25.78125 6 \r\nQ 29.828125 6 32.875 7.859375 \r\nQ 35.9375 9.71875 37.890625 12.8125 \r\nQ 39.84375 15.921875 41.15625 21.1875 \r\nQ 42.484375 26.46875 42.484375 31.9375 \r\nQ 42.484375 32.515625 42.4375 33.6875 \r\nQ 39.796875 29.5 35.234375 26.875 \r\nQ 30.671875 24.265625 25.34375 24.265625 \r\nQ 16.453125 24.265625 10.296875 30.703125 \r\nQ 4.15625 37.15625 4.15625 47.703125 \r\nQ 4.15625 58.59375 10.578125 65.234375 \r\nQ 17 71.875 26.65625 71.875 \r\nQ 33.640625 71.875 39.421875 68.109375 \r\nQ 45.21875 64.359375 48.21875 57.390625 \r\nQ 51.21875 50.4375 51.21875 37.25 \r\nQ 51.21875 23.53125 48.234375 15.40625 \r\nQ 45.265625 7.28125 39.375 3.03125 \r\nQ 33.5 -1.21875 25.59375 -1.21875 \r\nQ 17.1875 -1.21875 11.859375 3.4375 \r\nQ 6.546875 8.109375 5.46875 16.546875 \r\nz\r\nM 41.453125 48.140625 \r\nQ 41.453125 55.71875 37.421875 60.15625 \r\nQ 33.40625 64.59375 27.734375 64.59375 \r\nQ 21.875 64.59375 17.53125 59.8125 \r\nQ 13.1875 55.03125 13.1875 47.40625 \r\nQ 13.1875 40.578125 17.3125 36.296875 \r\nQ 21.4375 32.03125 27.484375 32.03125 \r\nQ 33.59375 32.03125 37.515625 36.296875 \r\nQ 41.453125 40.578125 41.453125 48.140625 \r\nz\r\n\" id=\"ArialMT-39\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(694.921875 470.393594)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-39\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_4\">\r\n     <g id=\"line2d_7\"/>\r\n    </g>\r\n    <g id=\"xtick_5\">\r\n     <g id=\"line2d_8\"/>\r\n    </g>\r\n    <g id=\"xtick_6\">\r\n     <g id=\"line2d_9\"/>\r\n    </g>\r\n    <g id=\"xtick_7\">\r\n     <g id=\"line2d_10\"/>\r\n    </g>\r\n    <g id=\"xtick_8\">\r\n     <g id=\"line2d_11\"/>\r\n    </g>\r\n    <g id=\"xtick_9\">\r\n     <g id=\"line2d_12\"/>\r\n    </g>\r\n    <g id=\"xtick_10\">\r\n     <g id=\"line2d_13\"/>\r\n    </g>\r\n    <g id=\"xtick_11\">\r\n     <g id=\"line2d_14\"/>\r\n    </g>\r\n    <g id=\"xtick_12\">\r\n     <g id=\"line2d_15\"/>\r\n    </g>\r\n    <g id=\"xtick_13\">\r\n     <g id=\"line2d_16\"/>\r\n     <g id=\"text_4\">\r\n      <!-- Jul -->\r\n      <defs>\r\n       <path d=\"M 40.578125 0 \r\nL 40.578125 7.625 \r\nQ 34.515625 -1.171875 24.125 -1.171875 \r\nQ 19.53125 -1.171875 15.546875 0.578125 \r\nQ 11.578125 2.34375 9.640625 5 \r\nQ 7.71875 7.671875 6.9375 11.53125 \r\nQ 6.390625 14.109375 6.390625 19.734375 \r\nL 6.390625 51.859375 \r\nL 15.1875 51.859375 \r\nL 15.1875 23.09375 \r\nQ 15.1875 16.21875 15.71875 13.8125 \r\nQ 16.546875 10.359375 19.234375 8.375 \r\nQ 21.921875 6.390625 25.875 6.390625 \r\nQ 29.828125 6.390625 33.296875 8.421875 \r\nQ 36.765625 10.453125 38.203125 13.9375 \r\nQ 39.65625 17.4375 39.65625 24.078125 \r\nL 39.65625 51.859375 \r\nL 48.4375 51.859375 \r\nL 48.4375 0 \r\nz\r\n\" id=\"ArialMT-75\"/>\r\n       <path d=\"M 6.390625 0 \r\nL 6.390625 71.578125 \r\nL 15.1875 71.578125 \r\nL 15.1875 0 \r\nz\r\n\" id=\"ArialMT-6c\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(253.252344 456.216719)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-4a\"/>\r\n       <use x=\"50\" xlink:href=\"#ArialMT-75\"/>\r\n       <use x=\"105.615234\" xlink:href=\"#ArialMT-6c\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_14\">\r\n     <g id=\"line2d_17\"/>\r\n    </g>\r\n    <g id=\"xtick_15\">\r\n     <g id=\"line2d_18\"/>\r\n    </g>\r\n    <g id=\"xtick_16\">\r\n     <g id=\"line2d_19\"/>\r\n    </g>\r\n    <g id=\"xtick_17\">\r\n     <g id=\"line2d_20\"/>\r\n    </g>\r\n    <g id=\"xtick_18\">\r\n     <g id=\"line2d_21\"/>\r\n    </g>\r\n    <g id=\"xtick_19\">\r\n     <g id=\"line2d_22\"/>\r\n    </g>\r\n    <g id=\"xtick_20\">\r\n     <g id=\"line2d_23\"/>\r\n    </g>\r\n    <g id=\"xtick_21\">\r\n     <g id=\"line2d_24\"/>\r\n    </g>\r\n    <g id=\"xtick_22\">\r\n     <g id=\"line2d_25\"/>\r\n    </g>\r\n    <g id=\"xtick_23\">\r\n     <g id=\"line2d_26\"/>\r\n    </g>\r\n    <g id=\"xtick_24\">\r\n     <g id=\"line2d_27\"/>\r\n    </g>\r\n    <g id=\"xtick_25\">\r\n     <g id=\"line2d_28\"/>\r\n     <g id=\"text_5\">\r\n      <!-- Jul -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(550.852344 456.216719)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-4a\"/>\r\n       <use x=\"50\" xlink:href=\"#ArialMT-75\"/>\r\n       <use x=\"105.615234\" xlink:href=\"#ArialMT-6c\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_26\">\r\n     <g id=\"line2d_29\"/>\r\n    </g>\r\n    <g id=\"xtick_27\">\r\n     <g id=\"line2d_30\"/>\r\n    </g>\r\n    <g id=\"xtick_28\">\r\n     <g id=\"line2d_31\"/>\r\n    </g>\r\n    <g id=\"xtick_29\">\r\n     <g id=\"line2d_32\"/>\r\n    </g>\r\n    <g id=\"xtick_30\">\r\n     <g id=\"line2d_33\"/>\r\n    </g>\r\n    <g id=\"xtick_31\">\r\n     <g id=\"line2d_34\"/>\r\n    </g>\r\n    <g id=\"xtick_32\">\r\n     <g id=\"line2d_35\"/>\r\n    </g>\r\n    <g id=\"xtick_33\">\r\n     <g id=\"line2d_36\"/>\r\n    </g>\r\n    <g id=\"xtick_34\">\r\n     <g id=\"line2d_37\"/>\r\n    </g>\r\n    <g id=\"xtick_35\">\r\n     <g id=\"line2d_38\"/>\r\n    </g>\r\n    <g id=\"xtick_36\">\r\n     <g id=\"line2d_39\"/>\r\n    </g>\r\n    <g id=\"xtick_37\">\r\n     <g id=\"line2d_40\"/>\r\n     <g id=\"text_6\">\r\n      <!-- Jul -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(848.452344 456.216719)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-4a\"/>\r\n       <use x=\"50\" xlink:href=\"#ArialMT-75\"/>\r\n       <use x=\"105.615234\" xlink:href=\"#ArialMT-6c\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_38\">\r\n     <g id=\"line2d_41\"/>\r\n    </g>\r\n    <g id=\"xtick_39\">\r\n     <g id=\"line2d_42\"/>\r\n    </g>\r\n    <g id=\"xtick_40\">\r\n     <g id=\"line2d_43\"/>\r\n    </g>\r\n    <g id=\"text_7\">\r\n     <!-- DATE -->\r\n     <defs>\r\n      <path d=\"M 7.71875 0 \r\nL 7.71875 71.578125 \r\nL 32.375 71.578125 \r\nQ 40.71875 71.578125 45.125 70.5625 \r\nQ 51.265625 69.140625 55.609375 65.4375 \r\nQ 61.28125 60.640625 64.078125 53.1875 \r\nQ 66.890625 45.75 66.890625 36.1875 \r\nQ 66.890625 28.03125 64.984375 21.734375 \r\nQ 63.09375 15.4375 60.109375 11.296875 \r\nQ 57.125 7.171875 53.578125 4.796875 \r\nQ 50.046875 2.4375 45.046875 1.21875 \r\nQ 40.046875 0 33.546875 0 \r\nz\r\nM 17.1875 8.453125 \r\nL 32.46875 8.453125 \r\nQ 39.546875 8.453125 43.578125 9.765625 \r\nQ 47.609375 11.078125 50 13.484375 \r\nQ 53.375 16.84375 55.25 22.53125 \r\nQ 57.125 28.21875 57.125 36.328125 \r\nQ 57.125 47.5625 53.4375 53.59375 \r\nQ 49.75 59.625 44.484375 61.671875 \r\nQ 40.671875 63.140625 32.234375 63.140625 \r\nL 17.1875 63.140625 \r\nz\r\n\" id=\"ArialMT-44\"/>\r\n      <path d=\"M -0.140625 0 \r\nL 27.34375 71.578125 \r\nL 37.546875 71.578125 \r\nL 66.84375 0 \r\nL 56.0625 0 \r\nL 47.703125 21.6875 \r\nL 17.78125 21.6875 \r\nL 9.90625 0 \r\nz\r\nM 20.515625 29.390625 \r\nL 44.78125 29.390625 \r\nL 37.3125 49.21875 \r\nQ 33.890625 58.25 32.234375 64.0625 \r\nQ 30.859375 57.171875 28.375 50.390625 \r\nz\r\n\" id=\"ArialMT-41\"/>\r\n      <path d=\"M 25.921875 0 \r\nL 25.921875 63.140625 \r\nL 2.34375 63.140625 \r\nL 2.34375 71.578125 \r\nL 59.078125 71.578125 \r\nL 59.078125 63.140625 \r\nL 35.40625 63.140625 \r\nL 35.40625 0 \r\nz\r\n\" id=\"ArialMT-54\"/>\r\n      <path d=\"M 7.90625 0 \r\nL 7.90625 71.578125 \r\nL 59.671875 71.578125 \r\nL 59.671875 63.140625 \r\nL 17.390625 63.140625 \r\nL 17.390625 41.21875 \r\nL 56.984375 41.21875 \r\nL 56.984375 32.8125 \r\nL 17.390625 32.8125 \r\nL 17.390625 8.453125 \r\nL 61.328125 8.453125 \r\nL 61.328125 0 \r\nz\r\n\" id=\"ArialMT-45\"/>\r\n     </defs>\r\n     <g style=\"fill:#262626;\" transform=\"translate(468.181094 484.254688)scale(0.11 -0.11)\">\r\n      <use xlink:href=\"#ArialMT-44\"/>\r\n      <use x=\"72.216797\" xlink:href=\"#ArialMT-41\"/>\r\n      <use x=\"138.806641\" xlink:href=\"#ArialMT-54\"/>\r\n      <use x=\"199.890625\" xlink:href=\"#ArialMT-45\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_2\">\r\n    <g id=\"ytick_1\">\r\n     <g id=\"line2d_44\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 36.44375 412.206599 \r\nL 929.24375 412.206599 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_45\"/>\r\n     <g id=\"text_8\">\r\n      <!-- 600 -->\r\n      <defs>\r\n       <path d=\"M 49.75 54.046875 \r\nL 41.015625 53.375 \r\nQ 39.84375 58.546875 37.703125 60.890625 \r\nQ 34.125 64.65625 28.90625 64.65625 \r\nQ 24.703125 64.65625 21.53125 62.3125 \r\nQ 17.390625 59.28125 14.984375 53.46875 \r\nQ 12.59375 47.65625 12.5 36.921875 \r\nQ 15.671875 41.75 20.265625 44.09375 \r\nQ 24.859375 46.4375 29.890625 46.4375 \r\nQ 38.671875 46.4375 44.84375 39.96875 \r\nQ 51.03125 33.5 51.03125 23.25 \r\nQ 51.03125 16.5 48.125 10.71875 \r\nQ 45.21875 4.9375 40.140625 1.859375 \r\nQ 35.0625 -1.21875 28.609375 -1.21875 \r\nQ 17.625 -1.21875 10.6875 6.859375 \r\nQ 3.765625 14.9375 3.765625 33.5 \r\nQ 3.765625 54.25 11.421875 63.671875 \r\nQ 18.109375 71.875 29.4375 71.875 \r\nQ 37.890625 71.875 43.28125 67.140625 \r\nQ 48.6875 62.40625 49.75 54.046875 \r\nz\r\nM 13.875 23.1875 \r\nQ 13.875 18.65625 15.796875 14.5 \r\nQ 17.71875 10.359375 21.1875 8.171875 \r\nQ 24.65625 6 28.46875 6 \r\nQ 34.03125 6 38.03125 10.484375 \r\nQ 42.046875 14.984375 42.046875 22.703125 \r\nQ 42.046875 30.125 38.078125 34.390625 \r\nQ 34.125 38.671875 28.125 38.671875 \r\nQ 22.171875 38.671875 18.015625 34.390625 \r\nQ 13.875 30.125 13.875 23.1875 \r\nz\r\n\" id=\"ArialMT-36\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(12.760938 415.785505)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-36\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_2\">\r\n     <g id=\"line2d_46\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 36.44375 345.301983 \r\nL 929.24375 345.301983 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_47\"/>\r\n     <g id=\"text_9\">\r\n      <!-- 800 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(12.760938 348.880889)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-38\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_3\">\r\n     <g id=\"line2d_48\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 36.44375 278.397368 \r\nL 929.24375 278.397368 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_49\"/>\r\n     <g id=\"text_10\">\r\n      <!-- 1000 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 281.976274)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_4\">\r\n     <g id=\"line2d_50\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 36.44375 211.492752 \r\nL 929.24375 211.492752 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_51\"/>\r\n     <g id=\"text_11\">\r\n      <!-- 1200 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 215.071659)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-32\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_52\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 36.44375 144.588137 \r\nL 929.24375 144.588137 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_53\"/>\r\n     <g id=\"text_12\">\r\n      <!-- 1400 -->\r\n      <defs>\r\n       <path d=\"M 32.328125 0 \r\nL 32.328125 17.140625 \r\nL 1.265625 17.140625 \r\nL 1.265625 25.203125 \r\nL 33.9375 71.578125 \r\nL 41.109375 71.578125 \r\nL 41.109375 25.203125 \r\nL 50.78125 25.203125 \r\nL 50.78125 17.140625 \r\nL 41.109375 17.140625 \r\nL 41.109375 0 \r\nz\r\nM 32.328125 25.203125 \r\nL 32.328125 57.46875 \r\nL 9.90625 25.203125 \r\nz\r\n\" id=\"ArialMT-34\"/>\r\n      </defs>\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 148.167043)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-34\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_54\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 36.44375 77.683522 \r\nL 929.24375 77.683522 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_55\"/>\r\n     <g id=\"text_13\">\r\n      <!-- 1600 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 81.262428)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-36\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_7\">\r\n     <g id=\"line2d_56\">\r\n      <path clip-path=\"url(#p80f12048e8)\" d=\"M 36.44375 10.778906 \r\nL 929.24375 10.778906 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"line2d_57\"/>\r\n     <g id=\"text_14\">\r\n      <!-- 1800 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(7.2 14.357812)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-31\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-38\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"line2d_58\">\r\n    <path clip-path=\"url(#p80f12048e8)\" d=\"M -1 186.386576 \r\nL 11.64375 265.350968 \r\nL 36.44375 383.437614 \r\nL 61.24375 373.736445 \r\nL 86.04375 198.446352 \r\nL 110.84375 153.62026 \r\nL 135.64375 390.128075 \r\nL 160.44375 384.10666 \r\nL 185.24375 376.412629 \r\nL 210.04375 358.348383 \r\nL 234.84375 384.441183 \r\nL 259.64375 396.484014 \r\nL 284.44375 153.954783 \r\nL 309.24375 283.080691 \r\nL 334.04375 389.124506 \r\nL 358.84375 384.441183 \r\nL 383.64375 227.884383 \r\nL 408.44375 188.41066 \r\nL 433.24375 382.434045 \r\nL 458.04375 373.736445 \r\nL 482.84375 382.434045 \r\nL 507.64375 356.006722 \r\nL 532.44375 376.412629 \r\nL 557.24375 385.444752 \r\nL 582.04375 155.292875 \r\nL 606.84375 286.425922 \r\nL 631.64375 371.394783 \r\nL 656.44375 361.359091 \r\nL 681.24375 188.076137 \r\nL 706.04375 232.902229 \r\nL 730.84375 397.487583 \r\nL 755.64375 390.797122 \r\nL 780.44375 374.405491 \r\nL 805.24375 357.679337 \r\nL 830.04375 388.120937 \r\nL 854.84375 396.818537 \r\nL 879.64375 202.795152 \r\nL 904.44375 294.454475 \r\nL 929.24375 375.40906 \r\nL 929.24375 375.40906 \r\n\" style=\"fill:none;stroke:#4c72b0;stroke-linecap:round;stroke-width:1.75;\"/>\r\n   </g>\r\n   <g id=\"patch_3\">\r\n    <path d=\"M 36.44375 445.658906 \r\nL 36.44375 10.778906 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_4\">\r\n    <path d=\"M 929.24375 445.658906 \r\nL 929.24375 10.778906 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_5\">\r\n    <path d=\"M 36.44375 445.658906 \r\nL 929.24375 445.658906 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"patch_6\">\r\n    <path d=\"M 36.44375 10.778906 \r\nL 929.24375 10.778906 \r\n\" style=\"fill:none;\"/>\r\n   </g>\r\n   <g id=\"legend_1\">\r\n    <g id=\"line2d_59\">\r\n     <path d=\"M 867.229687 23.436719 \r\nL 887.229687 23.436719 \r\n\" style=\"fill:none;stroke:#4c72b0;stroke-linecap:round;stroke-width:1.75;\"/>\r\n    </g>\r\n    <g id=\"line2d_60\"/>\r\n    <g id=\"text_15\">\r\n     <!-- Sales -->\r\n     <defs>\r\n      <path d=\"M 4.5 23 \r\nL 13.421875 23.78125 \r\nQ 14.0625 18.40625 16.375 14.96875 \r\nQ 18.703125 11.53125 23.578125 9.40625 \r\nQ 28.46875 7.28125 34.578125 7.28125 \r\nQ 39.984375 7.28125 44.140625 8.890625 \r\nQ 48.296875 10.5 50.3125 13.296875 \r\nQ 52.34375 16.109375 52.34375 19.4375 \r\nQ 52.34375 22.796875 50.390625 25.3125 \r\nQ 48.4375 27.828125 43.953125 29.546875 \r\nQ 41.0625 30.671875 31.203125 33.03125 \r\nQ 21.34375 35.40625 17.390625 37.5 \r\nQ 12.25 40.1875 9.734375 44.15625 \r\nQ 7.234375 48.140625 7.234375 53.078125 \r\nQ 7.234375 58.5 10.296875 63.203125 \r\nQ 13.375 67.921875 19.28125 70.359375 \r\nQ 25.203125 72.796875 32.421875 72.796875 \r\nQ 40.375 72.796875 46.453125 70.234375 \r\nQ 52.546875 67.671875 55.8125 62.6875 \r\nQ 59.078125 57.71875 59.328125 51.421875 \r\nL 50.25 50.734375 \r\nQ 49.515625 57.515625 45.28125 60.984375 \r\nQ 41.0625 64.453125 32.8125 64.453125 \r\nQ 24.21875 64.453125 20.28125 61.296875 \r\nQ 16.359375 58.15625 16.359375 53.71875 \r\nQ 16.359375 49.859375 19.140625 47.359375 \r\nQ 21.875 44.875 33.421875 42.265625 \r\nQ 44.96875 39.65625 49.265625 37.703125 \r\nQ 55.515625 34.8125 58.484375 30.390625 \r\nQ 61.46875 25.984375 61.46875 20.21875 \r\nQ 61.46875 14.5 58.203125 9.4375 \r\nQ 54.9375 4.390625 48.796875 1.578125 \r\nQ 42.671875 -1.21875 35.015625 -1.21875 \r\nQ 25.296875 -1.21875 18.71875 1.609375 \r\nQ 12.15625 4.4375 8.421875 10.125 \r\nQ 4.6875 15.828125 4.5 23 \r\nz\r\n\" id=\"ArialMT-53\"/>\r\n      <path d=\"M 42.09375 16.703125 \r\nL 51.171875 15.578125 \r\nQ 49.03125 7.625 43.21875 3.21875 \r\nQ 37.40625 -1.171875 28.375 -1.171875 \r\nQ 17 -1.171875 10.328125 5.828125 \r\nQ 3.65625 12.84375 3.65625 25.484375 \r\nQ 3.65625 38.578125 10.390625 45.796875 \r\nQ 17.140625 53.03125 27.875 53.03125 \r\nQ 38.28125 53.03125 44.875 45.953125 \r\nQ 51.46875 38.875 51.46875 26.03125 \r\nQ 51.46875 25.25 51.421875 23.6875 \r\nL 12.75 23.6875 \r\nQ 13.234375 15.140625 17.578125 10.59375 \r\nQ 21.921875 6.0625 28.421875 6.0625 \r\nQ 33.25 6.0625 36.671875 8.59375 \r\nQ 40.09375 11.140625 42.09375 16.703125 \r\nz\r\nM 13.234375 30.90625 \r\nL 42.1875 30.90625 \r\nQ 41.609375 37.453125 38.875 40.71875 \r\nQ 34.671875 45.796875 27.984375 45.796875 \r\nQ 21.921875 45.796875 17.796875 41.75 \r\nQ 13.671875 37.703125 13.234375 30.90625 \r\nz\r\n\" id=\"ArialMT-65\"/>\r\n      <path d=\"M 3.078125 15.484375 \r\nL 11.765625 16.84375 \r\nQ 12.5 11.625 15.84375 8.84375 \r\nQ 19.1875 6.0625 25.203125 6.0625 \r\nQ 31.25 6.0625 34.171875 8.515625 \r\nQ 37.109375 10.984375 37.109375 14.3125 \r\nQ 37.109375 17.28125 34.515625 19 \r\nQ 32.71875 20.171875 25.53125 21.96875 \r\nQ 15.875 24.421875 12.140625 26.203125 \r\nQ 8.40625 27.984375 6.46875 31.125 \r\nQ 4.546875 34.28125 4.546875 38.09375 \r\nQ 4.546875 41.546875 6.125 44.5 \r\nQ 7.71875 47.46875 10.453125 49.421875 \r\nQ 12.5 50.921875 16.03125 51.96875 \r\nQ 19.578125 53.03125 23.640625 53.03125 \r\nQ 29.734375 53.03125 34.34375 51.265625 \r\nQ 38.96875 49.515625 41.15625 46.5 \r\nQ 43.359375 43.5 44.1875 38.484375 \r\nL 35.59375 37.3125 \r\nQ 35.015625 41.3125 32.203125 43.546875 \r\nQ 29.390625 45.796875 24.265625 45.796875 \r\nQ 18.21875 45.796875 15.625 43.796875 \r\nQ 13.03125 41.796875 13.03125 39.109375 \r\nQ 13.03125 37.40625 14.109375 36.03125 \r\nQ 15.1875 34.625 17.484375 33.6875 \r\nQ 18.796875 33.203125 25.25 31.453125 \r\nQ 34.578125 28.953125 38.25 27.359375 \r\nQ 41.9375 25.78125 44.03125 22.75 \r\nQ 46.140625 19.734375 46.140625 15.234375 \r\nQ 46.140625 10.84375 43.578125 6.953125 \r\nQ 41.015625 3.078125 36.171875 0.953125 \r\nQ 31.34375 -1.171875 25.25 -1.171875 \r\nQ 15.140625 -1.171875 9.84375 3.03125 \r\nQ 4.546875 7.234375 3.078125 15.484375 \r\nz\r\n\" id=\"ArialMT-73\"/>\r\n     </defs>\r\n     <g style=\"fill:#262626;\" transform=\"translate(895.229687 26.936719)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#ArialMT-53\"/>\r\n      <use x=\"66.699219\" xlink:href=\"#ArialMT-61\"/>\r\n      <use x=\"122.314453\" xlink:href=\"#ArialMT-6c\"/>\r\n      <use x=\"144.53125\" xlink:href=\"#ArialMT-65\"/>\r\n      <use x=\"200.146484\" xlink:href=\"#ArialMT-73\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"p80f12048e8\">\r\n   <rect height=\"434.88\" width=\"892.8\" x=\"36.44375\" y=\"10.778906\"/>\r\n  </clipPath>\r\n </defs>\r\n</svg>\r\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAHtCAYAAAAUS1PeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "df.plot(figsize=(16,8))\n",
    "plt.xlim('2016-10-01','2019-10-01')\n",
    "plt.ylim(500,1800)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notes on book sales**\n",
    "- The sales trend appears to have seasonality.\n",
    "- Sales appear to peak in the two month window of December/January as well as in August.\n",
    "- Peak of August sales typically last for only that month with sales dramatically decreasing in September.\n",
    "- All remaining months have roughly the same value in sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "334"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Since we have clear seasonality in a yearly cycle, we want to include at least 12 months (12 data points) for our test size. 18 months was selected as it covers over a year to capture the seasonality trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 18\n",
    "test_ind = len(df) - test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.iloc[:test_ind]\n",
    "test = df.iloc[test_ind:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>316 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sales\n",
       "DATE             \n",
       "1992-01-01    790\n",
       "1992-02-01    539\n",
       "1992-03-01    535\n",
       "1992-04-01    523\n",
       "1992-05-01    552\n",
       "...           ...\n",
       "2017-12-01   1151\n",
       "2018-01-01   1269\n",
       "2018-02-01    689\n",
       "2018-03-01    715\n",
       "2018-04-01    689\n",
       "\n",
       "[316 rows x 1 columns]"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            Sales\nDATE             \n2018-05-01    768\n2018-06-01    707\n2018-07-01    680\n2018-08-01   1368\n2018-09-01    976\n2018-10-01    722\n2018-11-01    752\n2018-12-01   1270\n2019-01-01   1136\n2019-02-01    644\n2019-03-01    664\n2019-04-01    713\n2019-05-01    763\n2019-06-01    672\n2019-07-01    646\n2019-08-01   1226\n2019-09-01    952\n2019-10-01    710",
      "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sales</th>\n    </tr>\n    <tr>\n      <th>DATE</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-05-01</th>\n      <td>768</td>\n    </tr>\n    <tr>\n      <th>2018-06-01</th>\n      <td>707</td>\n    </tr>\n    <tr>\n      <th>2018-07-01</th>\n      <td>680</td>\n    </tr>\n    <tr>\n      <th>2018-08-01</th>\n      <td>1368</td>\n    </tr>\n    <tr>\n      <th>2018-09-01</th>\n      <td>976</td>\n    </tr>\n    <tr>\n      <th>2018-10-01</th>\n      <td>722</td>\n    </tr>\n    <tr>\n      <th>2018-11-01</th>\n      <td>752</td>\n    </tr>\n    <tr>\n      <th>2018-12-01</th>\n      <td>1270</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>1136</td>\n    </tr>\n    <tr>\n      <th>2019-02-01</th>\n      <td>644</td>\n    </tr>\n    <tr>\n      <th>2019-03-01</th>\n      <td>664</td>\n    </tr>\n    <tr>\n      <th>2019-04-01</th>\n      <td>713</td>\n    </tr>\n    <tr>\n      <th>2019-05-01</th>\n      <td>763</td>\n    </tr>\n    <tr>\n      <th>2019-06-01</th>\n      <td>672</td>\n    </tr>\n    <tr>\n      <th>2019-07-01</th>\n      <td>646</td>\n    </tr>\n    <tr>\n      <th>2019-08-01</th>\n      <td>1226</td>\n    </tr>\n    <tr>\n      <th>2019-09-01</th>\n      <td>952</td>\n    </tr>\n    <tr>\n      <th>2019-10-01</th>\n      <td>710</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train_scaled = scaler.fit_transform(train)\n",
    "test_scaled = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(316, 1)"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's redefine to go 12 months back and then predict the next month\n",
    "length = 12\n",
    "generator = TimeseriesGenerator(data=train_scaled,targets=train_scaled,\n",
    "                               length=length,batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(array([[0.14037855],\n        [0.0084122 ],\n        [0.00630915],\n        [0.        ],\n        [0.01524711],\n        [0.03470032],\n        [0.0362776 ],\n        [0.19505783],\n        [0.17770768],\n        [0.06414301],\n        [0.06256572],\n        [0.33753943],\n        [0.24973712],\n        [0.02365931]]), (array([[[0.14037855],\n          [0.0084122 ],\n          [0.00630915],\n          [0.        ],\n          [0.01524711],\n          [0.03470032],\n          [0.0362776 ],\n          [0.19505783],\n          [0.17770768],\n          [0.06414301],\n          [0.06256572],\n          [0.33753943]]]), array([[0.24973712]])))"
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "train_scaled[:14],generator[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=generator[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(array([[0.03470032],\n        [0.0362776 ],\n        [0.19505783],\n        [0.17770768],\n        [0.06414301],\n        [0.06256572],\n        [0.33753943],\n        [0.24973712],\n        [0.02365931],\n        [0.04153523],\n        [0.03154574],\n        [0.04679285]]), array([0.04994742]))"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "X[0],y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Given the Array: \n[[[0.0084122 ]\n  [0.00630915]\n  [0.        ]\n  [0.01524711]\n  [0.03470032]\n  [0.0362776 ]\n  [0.19505783]\n  [0.17770768]\n  [0.06414301]\n  [0.06256572]\n  [0.33753943]\n  [0.24973712]]]\nNext Predicted Value: \n [[0.02365931]]\n"
    }
   ],
   "source": [
    "print(f'Given the Array: \\n{X}')\n",
    "print(f'Next Predicted Value: \\n {y}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only one features (sales) is being used for this model\n",
    "n_features = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units=100,activation='relu',input_shape=(length,n_features)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm (LSTM)                  (None, 100)               40800     \n_________________________________________________________________\ndense (Dense)                (None, 1)                 101       \n=================================================================\nTotal params: 40,901\nTrainable params: 40,901\nNon-trainable params: 0\n_________________________________________________________________\n"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss',patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_genenerator = TimeseriesGenerator(data=test_scaled,targets=test_scaled,\n",
    "                                            length=length,batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "304/304 [==============================] - 6s 18ms/step - loss: 0.0504 - val_loss: 0.0121\n",
      "Epoch 2/20\n",
      "304/304 [==============================] - 6s 19ms/step - loss: 0.0407 - val_loss: 0.0103\n",
      "Epoch 3/20\n",
      "304/304 [==============================] - 6s 18ms/step - loss: 0.0240 - val_loss: 0.0084\n",
      "Epoch 4/20\n",
      "304/304 [==============================] - 5s 18ms/step - loss: 0.0117 - val_loss: 0.0023\n",
      "Epoch 5/20\n",
      "304/304 [==============================] - 6s 18ms/step - loss: 0.0063 - val_loss: 0.0011\n",
      "Epoch 6/20\n",
      "304/304 [==============================] - 6s 19ms/step - loss: 0.0048 - val_loss: 6.8227e-04\n",
      "Epoch 7/20\n",
      "304/304 [==============================] - 5s 18ms/step - loss: 0.0044 - val_loss: 4.2727e-04\n",
      "Epoch 8/20\n",
      "304/304 [==============================] - 6s 19ms/step - loss: 0.0041 - val_loss: 0.0020\n",
      "Epoch 9/20\n",
      "304/304 [==============================] - 5s 18ms/step - loss: 0.0042 - val_loss: 3.4972e-04\n",
      "Epoch 10/20\n",
      "304/304 [==============================] - 5s 18ms/step - loss: 0.0031 - val_loss: 7.2572e-04\n",
      "Epoch 11/20\n",
      "304/304 [==============================] - 6s 19ms/step - loss: 0.0036 - val_loss: 3.2723e-04\n",
      "Epoch 12/20\n",
      "304/304 [==============================] - 5s 18ms/step - loss: 0.0039 - val_loss: 7.0392e-04\n",
      "Epoch 13/20\n",
      "304/304 [==============================] - 5s 18ms/step - loss: 0.0035 - val_loss: 5.5885e-04\n",
      "Epoch 14/20\n",
      "304/304 [==============================] - 6s 19ms/step - loss: 0.0035 - val_loss: 4.3011e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1e27c5976c8>"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting model\n",
    "model.fit_generator(generator,epochs=20,validation_data=validation_genenerator,callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)\n",
    "losses.index = losses.index + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot(figsize=(10,6))\n",
    "plt.xlim(1,14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notes:**\n",
    "- Best results of the model was achieved after 11 epochs.\n",
    "- No improvements in validation loss was obtained with a patience of 3.\n",
    "- Several other iterations for training this model was attempted (e.g. varying test size (18-24), batch length size(12-18), and number of LSTM units (100-150).\n",
    "- The parameters found above yielded the best results according to the evaluation below, achieving the lowest root mean square error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of Model to Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = []\n",
    "\n",
    "first_eval_batch = train_scaled[-length:]\n",
    "current_batch = first_eval_batch.reshape((1, length, n_features))\n",
    "\n",
    "for i in range(len(test)):\n",
    "    \n",
    "    # get prediction 1 time stamp ahead ([0] is for grabbing just the number instead of [array])\n",
    "    current_pred = model.predict(current_batch)[0]\n",
    "    \n",
    "    # store prediction\n",
    "    test_predictions.append(current_pred) \n",
    "    \n",
    "    # update batch to now include prediction and drop first value\n",
    "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_predictions = scaler.inverse_transform(test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Users\\Charles\\Anaconda3\\envs\\mytfenv\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test['Predictions'] = true_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>768</td>\n",
       "      <td>714.788303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>707</td>\n",
       "      <td>665.931385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>680</td>\n",
       "      <td>649.010172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>1368</td>\n",
       "      <td>1310.781320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>976</td>\n",
       "      <td>949.257092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>722</td>\n",
       "      <td>672.266320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>752</td>\n",
       "      <td>671.561298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>1270</td>\n",
       "      <td>1111.055922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>1136</td>\n",
       "      <td>1259.176021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>644</td>\n",
       "      <td>695.404750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>664</td>\n",
       "      <td>697.217363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>713</td>\n",
       "      <td>675.645963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>763</td>\n",
       "      <td>683.305320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>672</td>\n",
       "      <td>656.151748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>646</td>\n",
       "      <td>647.705787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1226</td>\n",
       "      <td>1255.693015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>952</td>\n",
       "      <td>907.040735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>710</td>\n",
       "      <td>669.894675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sales  Predictions\n",
       "DATE                          \n",
       "2018-05-01    768   714.788303\n",
       "2018-06-01    707   665.931385\n",
       "2018-07-01    680   649.010172\n",
       "2018-08-01   1368  1310.781320\n",
       "2018-09-01    976   949.257092\n",
       "2018-10-01    722   672.266320\n",
       "2018-11-01    752   671.561298\n",
       "2018-12-01   1270  1111.055922\n",
       "2019-01-01   1136  1259.176021\n",
       "2019-02-01    644   695.404750\n",
       "2019-03-01    664   697.217363\n",
       "2019-04-01    713   675.645963\n",
       "2019-05-01    763   683.305320\n",
       "2019-06-01    672   656.151748\n",
       "2019-07-01    646   647.705787\n",
       "2019-08-01   1226  1255.693015\n",
       "2019-09-01    952   907.040735\n",
       "2019-10-01    710   669.894675"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual Sales vs. Predicted Sales from Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(figsize=(10,6))\n",
    "plt.ylim(500,1550)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.59487142133888"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "np.sqrt(mean_squared_error(test['Sales'],test['Predictions']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the order of magnitude of sales, a root mean square error of 64.6 seems like an acceptable value for this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting into the end of 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All data will now be used to train a new model.\n",
    "full_scaler = MinMaxScaler()\n",
    "full_data_scaled = full_scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 12\n",
    "generator_full_data = TimeseriesGenerator(full_data_scaled,full_data_scaled,\n",
    "                               length=length,batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0465\n",
      "Epoch 2/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0316\n",
      "Epoch 3/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0180\n",
      "Epoch 4/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0062\n",
      "Epoch 5/11\n",
      "322/322 [==============================] - 6s 20ms/step - loss: 0.0039\n",
      "Epoch 6/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0039\n",
      "Epoch 7/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0034\n",
      "Epoch 8/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0030\n",
      "Epoch 9/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0030\n",
      "Epoch 10/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0032\n",
      "Epoch 11/11\n",
      "322/322 [==============================] - 6s 18ms/step - loss: 0.0031\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1e27cb55288>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units=100,activation='relu',input_shape=(length,n_features)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam',loss='mse')\n",
    "\n",
    "# 11 epochs was selected since the previous model above yielded the lowest loss after 11 epochs.\n",
    "model.fit_generator(generator_full_data,epochs=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = []\n",
    "# We will forecast into the next 14 months to give us sales predictions through the end of 2020.\n",
    "periods = 14\n",
    "\n",
    "first_eval_batch = full_data_scaled[-length:]\n",
    "current_batch = first_eval_batch.reshape((1, length, n_features))\n",
    "\n",
    "for i in range(periods):\n",
    "    \n",
    "    # get prediction 1 time stamp ahead ([0] is for grabbing just the number instead of [array])\n",
    "    current_pred = model.predict(current_batch)[0]\n",
    "    \n",
    "    # store prediction\n",
    "    forecast.append(current_pred) \n",
    "    \n",
    "    # update batch to now include prediction and drop first value\n",
    "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = scaler.inverse_transform(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 768.92241818],\n",
       "       [1320.53584588],\n",
       "       [1193.05088377],\n",
       "       [ 707.3288849 ],\n",
       "       [ 707.4081575 ],\n",
       "       [ 734.40672654],\n",
       "       [ 772.02802211],\n",
       "       [ 702.78197646],\n",
       "       [ 687.36789167],\n",
       "       [1264.63565528],\n",
       "       [ 985.9061718 ],\n",
       "       [ 738.84780592],\n",
       "       [ 785.1976749 ],\n",
       "       [1378.34815842]])"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_index = pd.date_range(start='2019-11-01',periods=periods,\n",
    "                              freq='MS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_df = pd.DataFrame(data=forecast,index=forecast_index,\n",
    "                          columns=['Forecast'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Forecasted sales through the end of 2020 using our model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>768.922418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>1320.535846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1193.050884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>707.328885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>707.408157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>734.406727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>772.028022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>702.781976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>687.367892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>1264.635655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>985.906172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>738.847806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>785.197675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>1378.348158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Forecast\n",
       "2019-11-01   768.922418\n",
       "2019-12-01  1320.535846\n",
       "2020-01-01  1193.050884\n",
       "2020-02-01   707.328885\n",
       "2020-03-01   707.408157\n",
       "2020-04-01   734.406727\n",
       "2020-05-01   772.028022\n",
       "2020-06-01   702.781976\n",
       "2020-07-01   687.367892\n",
       "2020-08-01  1264.635655\n",
       "2020-09-01   985.906172\n",
       "2020-10-01   738.847806\n",
       "2020-11-01   785.197675\n",
       "2020-12-01  1378.348158"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(figsize=(12,8))\n",
    "forecast_df.plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(figsize=(14,8))\n",
    "forecast_df.plot(ax=ax)\n",
    "plt.xlim('2018-01-01','2020-12-01')\n",
    "plt.ylim(500,1600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Final Thoughts:**\n",
    "- Projecting out to the end of 2020, we can still expect the same trend in seasonality with book sales peaking twice per year: the December/January peak as well as the August sales peak.\n",
    "- Interestingly, although predicted sales in late 2019 and all of 2020 follow the same seasonal trend, the overall sales for that predicted time period appears to be sligthly greater than years past.\n",
    "- Perhaps an upswing in book sales will begin to take place, climbing back up to where overall sales were the highest in 2004-2009.\n",
    "- Alternatively, sales may begin to plateau after 2019 and the overall decline starting from 2009 has finally stopped for the time being.\n",
    "- It is difficult to project the overall trend that far out into the future with the data available."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}